Ted_2_NLP_caption.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transcript and NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in captions_f.json to selectively run cells from session 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "import unidecode\n",
    "%matplotlib inline\n",
    "import copy\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import sent_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from pattern import web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the transcripts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a function to extract caption info and put into a df\n",
    "def capdfget(talkid):\n",
    "    capurl = 'https://api.ted.com/v1/talks/'+str(talkid)+'/captions.json?api-key=ynw2u8e4h9sk8c2htp7vutxq'\n",
    "    capresponse = requests.get(capurl)\n",
    "    try:\n",
    "        captions = capresponse.json()['captions']\n",
    "    except KeyError:\n",
    "        print 'no captions, talkid: {}'.format(talkid)\n",
    "        return\n",
    "    captions = [x['caption'] for x in captions]\n",
    "    capdf = pd.DataFrame(captions)\n",
    "    capdf['id'] = talkid\n",
    "    return capdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cap_talk1 = capdfget(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cap_talk1.columns = ['Subtitle','Duration (ms)','Start of Paragraph', 'Start Time (ms)','id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subtitle</th>\n",
       "      <th>Duration (ms)</th>\n",
       "      <th>Start of Paragraph</th>\n",
       "      <th>Start Time (ms)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thank you so much, Chris. And it's truly a gre...</td>\n",
       "      <td>4000</td>\n",
       "      <td>True</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>to have the opportunity to come to this stage ...</td>\n",
       "      <td>4000</td>\n",
       "      <td>False</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I have been blown away by this conference, and...</td>\n",
       "      <td>7000</td>\n",
       "      <td>False</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nice comments about what I had to say the othe...</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>And I say that sincerely, partly because -- (M...</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Put yourselves in my position!</td>\n",
       "      <td>8000</td>\n",
       "      <td>False</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I flew on Air Force Two for eight years.</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Now I have to take off my shoes or boots to ge...</td>\n",
       "      <td>3000</td>\n",
       "      <td>False</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Laughter) (Applause)</td>\n",
       "      <td>9000</td>\n",
       "      <td>False</td>\n",
       "      <td>51000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I'll tell you one quick story to illustrate wh...</td>\n",
       "      <td>7000</td>\n",
       "      <td>True</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>It's a true story -- every bit of this is true.</td>\n",
       "      <td>3000</td>\n",
       "      <td>False</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Subtitle  Duration (ms)  \\\n",
       "0   Thank you so much, Chris. And it's truly a gre...           4000   \n",
       "1   to have the opportunity to come to this stage ...           4000   \n",
       "2   I have been blown away by this conference, and...           7000   \n",
       "3   nice comments about what I had to say the othe...           5000   \n",
       "4   And I say that sincerely, partly because -- (M...          12000   \n",
       "5                      Put yourselves in my position!           8000   \n",
       "6            I flew on Air Force Two for eight years.           5000   \n",
       "7   Now I have to take off my shoes or boots to ge...           3000   \n",
       "8                               (Laughter) (Applause)           9000   \n",
       "9   I'll tell you one quick story to illustrate wh...           7000   \n",
       "10    It's a true story -- every bit of this is true.           3000   \n",
       "\n",
       "   Start of Paragraph  Start Time (ms)  \n",
       "0                True             3000  \n",
       "1               False             7000  \n",
       "2               False            11000  \n",
       "3               False            18000  \n",
       "4               False            23000  \n",
       "5               False            35000  \n",
       "6               False            43000  \n",
       "7               False            48000  \n",
       "8               False            51000  \n",
       "9                True            60000  \n",
       "10              False            67000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cap_talk1.drop('id',axis = 1).head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the ids that are orignal editted by TED "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tedids = pd.read_csv('tedtalkid.csv',header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tedids.columns = ['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "idlist = list(tedids['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1905"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(idlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no captions, talkid: 24\n",
      "no captions, talkid: 138\n",
      "no captions, talkid: 169\n",
      "no captions, talkid: 186\n",
      "no captions, talkid: 265\n",
      "no captions, talkid: 290\n",
      "no captions, talkid: 478\n",
      "no captions, talkid: 541\n",
      "no captions, talkid: 581\n",
      "no captions, talkid: 631\n",
      "no captions, talkid: 672\n",
      "no captions, talkid: 745\n",
      "no captions, talkid: 787\n",
      "no captions, talkid: 870\n",
      "no captions, talkid: 1358\n",
      "no captions, talkid: 1418\n",
      "no captions, talkid: 1439\n",
      "no captions, talkid: 2253\n",
      "--- 2353.15383315 seconds ---\n"
     ]
    }
   ],
   "source": [
    "# loop to get all captions\n",
    "import time\n",
    "tedcap_list = []\n",
    "start_time = time.time()\n",
    "for talkid in idlist:\n",
    "    time.sleep(0.3)\n",
    "    tedcap = capdfget(talkid)\n",
    "    tedcap_list.append(tedcap)\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1905"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tedcap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nocaps = [24,138,169,186,265,290,478,541,581,631,672,745,787,870,1358,1418,1439,2253]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tedcap_list = [x for x in tedcap_list if x is not None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1905"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tedcap_list) + len(nocaps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tedcapdf = pd.concat(tedcap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>duration</th>\n",
       "      <th>startOfParagraph</th>\n",
       "      <th>startTime</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thank you so much, Chris. And it's truly a gre...</td>\n",
       "      <td>4000</td>\n",
       "      <td>True</td>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>to have the opportunity to come to this stage ...</td>\n",
       "      <td>4000</td>\n",
       "      <td>False</td>\n",
       "      <td>7000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I have been blown away by this conference, and...</td>\n",
       "      <td>7000</td>\n",
       "      <td>False</td>\n",
       "      <td>11000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nice comments about what I had to say the othe...</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>18000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>And I say that sincerely, partly because -- (M...</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  duration  \\\n",
       "0  Thank you so much, Chris. And it's truly a gre...      4000   \n",
       "1  to have the opportunity to come to this stage ...      4000   \n",
       "2  I have been blown away by this conference, and...      7000   \n",
       "3  nice comments about what I had to say the othe...      5000   \n",
       "4  And I say that sincerely, partly because -- (M...     12000   \n",
       "\n",
       "  startOfParagraph  startTime  id  \n",
       "0             True       3000   1  \n",
       "1            False       7000   1  \n",
       "2            False      11000   1  \n",
       "3            False      18000   1  \n",
       "4            False      23000   1  "
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tedcapdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 525171 entries, 0 to 183\n",
      "Data columns (total 5 columns):\n",
      "content             525171 non-null object\n",
      "duration            525171 non-null int64\n",
      "startOfParagraph    525171 non-null bool\n",
      "startTime           525171 non-null int64\n",
      "id                  525171 non-null int64\n",
      "dtypes: bool(1), int64(3), object(1)\n",
      "memory usage: 20.5+ MB\n"
     ]
    }
   ],
   "source": [
    "tedcapdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tedcapdf.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tedcapdf.set_index(['id','index'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tedcapdf.to_csv('tedcaption.csv',encoding = 'utf-8',)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Read in the caption data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "capdata = pd.read_csv('tedcaption.csv',encoding = 'utf-8',index_col = ['id','index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Derive some basic features such as: duration, words/time, number of paragraphs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>duration</th>\n",
       "      <th>startOfParagraph</th>\n",
       "      <th>startTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>Thank you so much, Chris. And it's truly a gre...</td>\n",
       "      <td>4000</td>\n",
       "      <td>True</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>to have the opportunity to come to this stage ...</td>\n",
       "      <td>4000</td>\n",
       "      <td>False</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I have been blown away by this conference, and...</td>\n",
       "      <td>7000</td>\n",
       "      <td>False</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nice comments about what I had to say the othe...</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>And I say that sincerely, partly because -- (M...</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    content  duration  \\\n",
       "id index                                                                \n",
       "1  0      Thank you so much, Chris. And it's truly a gre...      4000   \n",
       "   1      to have the opportunity to come to this stage ...      4000   \n",
       "   2      I have been blown away by this conference, and...      7000   \n",
       "   3      nice comments about what I had to say the othe...      5000   \n",
       "   4      And I say that sincerely, partly because -- (M...     12000   \n",
       "\n",
       "         startOfParagraph  startTime  \n",
       "id index                              \n",
       "1  0                 True       3000  \n",
       "   1                False       7000  \n",
       "   2                False      11000  \n",
       "   3                False      18000  \n",
       "   4                False      23000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capdata.ix[1].loc[0]['startTime']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. talk length "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lengths = []\n",
    "for ind in capdata.index.levels[0].values:\n",
    "    length = capdata.ix[ind].iloc[-1]['startTime'] - capdata.ix[ind].iloc[0]['startTime'] \n",
    "    + capdata.ix[ind].iloc[-1]['duration']\n",
    "    length = length / 1000.\n",
    "    lengths.append(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1887"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####2.  words count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# very raw version, ignore special characters,\n",
    "# ignore parenthesis at this moment\n",
    "# test = capdata.ix[1].iloc[0]['content']\n",
    "capdata['wordscount']=capdata['content'].apply(lambda x: len(x.split(' ')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wordscount = capdata['wordscount'].sum(level = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1887.000000\n",
       "mean     2095.108108\n",
       "std      1007.078145\n",
       "min         2.000000\n",
       "25%      1281.500000\n",
       "50%      2145.000000\n",
       "75%      2828.000000\n",
       "max      6501.000000\n",
       "Name: wordscount, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordscount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####3. number of paragraph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_para = capdata.startOfParagraph.sum(level = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1887.000000\n",
       "mean       24.385268\n",
       "std        19.056579\n",
       "min         1.000000\n",
       "25%        15.000000\n",
       "50%        21.000000\n",
       "75%        29.000000\n",
       "max       276.000000\n",
       "Name: startOfParagraph, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_para.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Form a capstat dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "capstat = pd.DataFrame(dict(wordcounts = wordscount, num_para = num_para, length = lengths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1887 entries, 1 to 2251\n",
      "Data columns (total 3 columns):\n",
      "length        1887 non-null float64\n",
      "num_para      1887 non-null float64\n",
      "wordcounts    1887 non-null int64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 59.0 KB\n"
     ]
    }
   ],
   "source": [
    "capstat.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import talks and save the ones that have captions into two final jsons, big and small talks df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read in the talks_small.json\n",
    "talks_f = pd.read_json('talks_f.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talks_f.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talks_small = talks_f.drop(['slug','event_name','image_16x9','images','media','tags'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1976, 31)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talks_small.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'length', u'num_para', u'wordcounts'], dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capstat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>944</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>879</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1099</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1152</td>\n",
       "      <td>28</td>\n",
       "      <td>3059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1177</td>\n",
       "      <td>28</td>\n",
       "      <td>3760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    length  num_para  wordcounts\n",
       "id                              \n",
       "1      944        16        2139\n",
       "2      879        27        2604\n",
       "3     1099        22        2485\n",
       "4     1152        28        3059\n",
       "5     1177        28        3760"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capstat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# great a mask to select only talks with captions\n",
    "capmask = talks_small['id'].isin(capstat.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1885"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capmask.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###talk_df_small and talk_df_large serve as final dataset (talks all with captions) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "talk_df_small = talks_small[capmask].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1885, 31)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_df_small.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before saving talk_df_small, process the keywords and description columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "keywords = talk_df_small.keywords.apply(lambda x:' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "talk_df_small.drop('keywords',axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "talk_df_small['keywords']= keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Presentation Innovation A Greener Future? Bold...\n",
       "1    A Greener Future? Tales of Invention Rethinkin...\n",
       "2    What's Next in Tech TED Conference TED Fellows...\n",
       "4    Words About Words TEDx creativity crime educat...\n",
       "5    Evolution's Genius How the Mind Works Unconven...\n",
       "Name: keywords, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_df_small.keywords[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talk_df_large = talks_f[talks_f['id'].isin(talk_df_small['id'])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "talk_df_large['keywords'] = talk_df_small['keywords']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talk_df_large.to_json('talk_df_large.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talk_df_small.to_json('talk_df_small.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Note 2 talks were not updated when the general dataframe was downloaded, they are: id = 2249, 2251"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_talk_ids = set(capstat.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "old_talk_ids = set(talks_small['id'][capmask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_talks = new_talk_ids.difference(old_talk_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2249, 2251}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_talks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp = capstat[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>334.857</td>\n",
       "      <td>19</td>\n",
       "      <td>1049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>233.380</td>\n",
       "      <td>8</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>1054.237</td>\n",
       "      <td>42</td>\n",
       "      <td>2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>940.533</td>\n",
       "      <td>28</td>\n",
       "      <td>2323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>546.408</td>\n",
       "      <td>66</td>\n",
       "      <td>1022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        length  num_para  wordcounts\n",
       "id                                  \n",
       "2244   334.857        19        1049\n",
       "2245   233.380         8         614\n",
       "2246  1054.237        42        2382\n",
       "2247   940.533        28        2323\n",
       "2248   546.408        66        1022"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>944</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>879</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1099</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1152</td>\n",
       "      <td>28</td>\n",
       "      <td>3059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1177</td>\n",
       "      <td>28</td>\n",
       "      <td>3760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>983</td>\n",
       "      <td>20</td>\n",
       "      <td>2492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1257</td>\n",
       "      <td>28</td>\n",
       "      <td>3325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1440</td>\n",
       "      <td>81</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1179</td>\n",
       "      <td>1</td>\n",
       "      <td>3288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>179</td>\n",
       "      <td>7</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  length  num_para  wordcounts\n",
       "0   1     944        16        2139\n",
       "1   2     879        27        2604\n",
       "2   3    1099        22        2485\n",
       "3   4    1152        28        3059\n",
       "4   5    1177        28        3760\n",
       "5   6     983        20        2492\n",
       "6   7    1257        28        3325\n",
       "7   8    1440        81        3916\n",
       "8   9    1179         1        3288\n",
       "9  10     179         7         618"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captalks = talks_small[['viewed_count','rating_count','commented_count','id']][capmask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1885, 4)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captalks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captalks.sort(inplace = True, columns = 'id',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "capstat.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captalks_f = pd.merge(captalks,capstat,how = 'inner',on = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>viewed_count</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>commented_count</th>\n",
       "      <th>id</th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2842062</td>\n",
       "      <td>2485</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>944</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>956392</td>\n",
       "      <td>1511</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>879</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650873</td>\n",
       "      <td>772</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>1099</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1614164</td>\n",
       "      <td>1806</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>1152</td>\n",
       "      <td>28</td>\n",
       "      <td>3059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711615</td>\n",
       "      <td>861</td>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
       "      <td>1177</td>\n",
       "      <td>28</td>\n",
       "      <td>3760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   viewed_count  rating_count  commented_count  id  length  num_para  \\\n",
       "0       2842062          2485              244   1     944        16   \n",
       "1        956392          1511               78   2     879        27   \n",
       "2        650873           772               61   3    1099        22   \n",
       "3       1614164          1806              180   4    1152        28   \n",
       "4        711615           861               74   5    1177        28   \n",
       "\n",
       "   wordcounts  \n",
       "0        2139  \n",
       "1        2604  \n",
       "2        2485  \n",
       "3        3059  \n",
       "4        3760  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captalks_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captalks_f['word_per_sec'] = captalks_f['wordcounts']/captalks_f['length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>viewed_count</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>commented_count</th>\n",
       "      <th>id</th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "      <th>word_per_sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2842062</td>\n",
       "      <td>2485</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>944</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "      <td>2.265890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>956392</td>\n",
       "      <td>1511</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>879</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "      <td>2.962457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650873</td>\n",
       "      <td>772</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>1099</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "      <td>2.261146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1614164</td>\n",
       "      <td>1806</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>1152</td>\n",
       "      <td>28</td>\n",
       "      <td>3059</td>\n",
       "      <td>2.655382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711615</td>\n",
       "      <td>861</td>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
       "      <td>1177</td>\n",
       "      <td>28</td>\n",
       "      <td>3760</td>\n",
       "      <td>3.194562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   viewed_count  rating_count  commented_count  id  length  num_para  \\\n",
       "0       2842062          2485              244   1     944        16   \n",
       "1        956392          1511               78   2     879        27   \n",
       "2        650873           772               61   3    1099        22   \n",
       "3       1614164          1806              180   4    1152        28   \n",
       "4        711615           861               74   5    1177        28   \n",
       "\n",
       "   wordcounts  word_per_sec  \n",
       "0        2139      2.265890  \n",
       "1        2604      2.962457  \n",
       "2        2485      2.261146  \n",
       "3        3059      2.655382  \n",
       "4        3760      3.194562  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captalks_f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save the captalks_f as json "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captalks_f.to_json('captalks_f.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captalks_f.set_index('id',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x127901510>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pWhAPg2es+7OhOgfaitXe+oOeXHzrcwFz9d3WsUIUD9OEmlo4lAKvHoZX/d7o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\nkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127b29c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,6))\n",
    "ax1 = plt.gca()\n",
    "ax1.scatter( x = captalks_f.length, y = captalks_f.viewed_count,\n",
    "                marker = 'o', alpha = 0.3)\n",
    "ax1.set_yscale('log')\n",
    "plt.xlabel('talk length in sec')\n",
    "plt.ylabel('log of total view counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_vs_cap(count,stat,ax):\n",
    "    ax.set_yscale('log')\n",
    "    ax.scatter(x = captalks_f[stat], y = captalks_f[count], marker = 'o', alpha = 0.2, label = count+' vs '+stat)\n",
    "    ax.legend(loc = 'best',fontsize = 'large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = list(captalks_f.columns[:3].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stats = ['length','num_para','word_per_sec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aAKwsg+AOOI1J/ValKyLDWeo3trGj4SaD8FYvbFwO1+XBB4ESH3zpV7AlZqHu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iHrqxtkX9r8GRbDjogY2VQ+elH+1YeOA49jtp8JW2IYqXHO6/42JnHtyQD8exIinL3fDqRTsz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XxeH0s3FH/1UR6+mgVxPn6EgXWxWOyxcIw727noJi6tZl0eG8tYu5NlCe5JT4Cl8Pd1NNYE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\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ngv5tyWT42jPCppGwt3QlKElyNMfaFDsO8ZG+LVvy2HWQq1JBWHviBHuH2TnGz+0Me+An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\nG1N8fvOp+U1uRcgZswXG4Xn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l2pdJOoLsxqs57EWIMJMRMUUjRIhwMqZKinyxYSLvOL8j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A7YKcBg9YrXnlb//ewSGpKBbgcczbaD0WoHYH4bXN1pB5tcXww9+bDeCQcDsO2gzeUihvan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\n/g/m+Bt0OreveQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102c67dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes=plt.subplots(figsize = (18,12),nrows=3, ncols=3)\n",
    "for ind in xrange(9):\n",
    "    i = ind/3;j = ind%3\n",
    "    ax = axes[i][j]\n",
    "    count_vs_cap(counts[i],stats[j],ax)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### raw dist plots of cap features so far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>viewed_count</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>commented_count</th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "      <th>word_per_sec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2842062</td>\n",
       "      <td>2485</td>\n",
       "      <td>244</td>\n",
       "      <td>944</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "      <td>2.265890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>956392</td>\n",
       "      <td>1511</td>\n",
       "      <td>78</td>\n",
       "      <td>879</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "      <td>2.962457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>650873</td>\n",
       "      <td>772</td>\n",
       "      <td>61</td>\n",
       "      <td>1099</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "      <td>2.261146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1614164</td>\n",
       "      <td>1806</td>\n",
       "      <td>180</td>\n",
       "      <td>1152</td>\n",
       "      <td>28</td>\n",
       "      <td>3059</td>\n",
       "      <td>2.655382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>711615</td>\n",
       "      <td>861</td>\n",
       "      <td>74</td>\n",
       "      <td>1177</td>\n",
       "      <td>28</td>\n",
       "      <td>3760</td>\n",
       "      <td>3.194562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    viewed_count  rating_count  commented_count  length  num_para  wordcounts  \\\n",
       "id                                                                              \n",
       "1        2842062          2485              244     944        16        2139   \n",
       "2         956392          1511               78     879        27        2604   \n",
       "3         650873           772               61    1099        22        2485   \n",
       "4        1614164          1806              180    1152        28        3059   \n",
       "5         711615           861               74    1177        28        3760   \n",
       "\n",
       "    word_per_sec  \n",
       "id                \n",
       "1       2.265890  \n",
       "2       2.962457  \n",
       "3       2.261146  \n",
       "4       2.655382  \n",
       "5       3.194562  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captalks_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S703ysiT/OMndSW5McjTJfUmu6O7Fqro0yWVJjiS5urtvmU3FAABsFic5z3Rirmk2OAEh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102c67890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes=plt.subplots(figsize = (18,6),nrows=1, ncols=3)\n",
    "for ind in xrange(3):\n",
    "    ax = axes[ind]\n",
    "    captalks_f[stats[ind]].hist(bins = 30, ax = ax, label = stats[ind])\n",
    "    ax.legend(loc = 'best',fontsize = 'large')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax = axes[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##2. Combine with Ted_4 to mining features from the transcripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "response = pd.read_csv('response5.csv',header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "response.columns = ['id','label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### select captions from talks in the final training set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ids = list(response['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions = capdata[capdata.index.get_level_values('id').isin(ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1535"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(captions.index.get_level_values('id').unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captest = captions[captions.index.get_level_values('id')==114]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(captest == '(Laughter)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus = []\n",
    "for idx in ids:\n",
    "    text = []\n",
    "    for index,row in captions[captions.index.get_level_values('id')==idx].iterrows():\n",
    "        text.append(row['content'])\n",
    "    corpus.append(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1535"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f = pd.DataFrame(dict(id = ids, text = corpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[Thank you so much, Chris. And it's truly a gr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                               text\n",
       "0   1  [Thank you so much, Chris. And it's truly a gr..."
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535, 2)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2.1 Find how many \"(Laughter)\" and \"(Applause)\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####2.1.1Total number of laughter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(re.findall(r'(Laughter)',captions_f.text[0][4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def findalllaughter(text):\n",
    "    content = ';'.join(text)\n",
    "    num = len(re.findall(r'(Laughter)',content))\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findalllaughter(captions_f.text[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "laughter_total = captions_f.text.apply(findalllaughter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['laughter_total'] = laughter_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>laughter_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[Thank you so much, Chris. And it's truly a gr...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[In terms of invention,, I'd like to tell you ...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2167</td>\n",
       "      <td>[When you grow up in a developing country\\nlik...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[A public, Dewey long ago observed,, is consti...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99</td>\n",
       "      <td>[Okay., ♫ Strolling along in Central Park ♫, ♫...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                               text  laughter_total\n",
       "0     1  [Thank you so much, Chris. And it's truly a gr...              10\n",
       "1     2  [In terms of invention,, I'd like to tell you ...               6\n",
       "2  2167  [When you grow up in a developing country\\nlik...               0\n",
       "3     3  [A public, Dewey long ago observed,, is consti...               1\n",
       "4    99  [Okay., ♫ Strolling along in Central Park ♫, ♫...               2"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = capstat[capstat['id'].isin(ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535, 4)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f = pd.merge(captions_f, capstat, on = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>laughter_total</th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[Thank you so much, Chris. And it's truly a gr...</td>\n",
       "      <td>10</td>\n",
       "      <td>944.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[In terms of invention,, I'd like to tell you ...</td>\n",
       "      <td>6</td>\n",
       "      <td>879.000</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2167</td>\n",
       "      <td>[When you grow up in a developing country\\nlik...</td>\n",
       "      <td>0</td>\n",
       "      <td>967.755</td>\n",
       "      <td>25</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[A public, Dewey long ago observed,, is consti...</td>\n",
       "      <td>1</td>\n",
       "      <td>1099.000</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99</td>\n",
       "      <td>[Okay., ♫ Strolling along in Central Park ♫, ♫...</td>\n",
       "      <td>2</td>\n",
       "      <td>131.000</td>\n",
       "      <td>28</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                               text  laughter_total  \\\n",
       "0     1  [Thank you so much, Chris. And it's truly a gr...              10   \n",
       "1     2  [In terms of invention,, I'd like to tell you ...               6   \n",
       "2  2167  [When you grow up in a developing country\\nlik...               0   \n",
       "3     3  [A public, Dewey long ago observed,, is consti...               1   \n",
       "4    99  [Okay., ♫ Strolling along in Central Park ♫, ♫...               2   \n",
       "\n",
       "     length  num_para  wordcounts  \n",
       "0   944.000        16        2139  \n",
       "1   879.000        27        2604  \n",
       "2   967.755        25        1992  \n",
       "3  1099.000        22        2485  \n",
       "4   131.000        28         173  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['laughter_per_word'] = captions_f['laughter_total']/captions_f.wordcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['word_per_sec'] = captions_f['wordcounts']/captions_f.length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['para_length'] = captions_f['wordcounts']/captions_f['num_para']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####2.1.2 How many applause "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def findallapplause(text):\n",
    "    content = ';'.join(text)\n",
    "    num = len(re.findall(r'(Applause)',content))\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "applause_total = captions_f.text.apply(findallapplause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([               u'id',              u'text',    u'laughter_total',\n",
       "                  u'length',          u'num_para',        u'wordcounts',\n",
       "       u'laughter_per_word',      u'word_per_sec',       u'para_length'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['applause_total']= applause_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1535.000000\n",
       "mean        2.168730\n",
       "std         1.992702\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         3.000000\n",
       "max        17.000000\n",
       "Name: applause_total, dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.applause_total.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions_f['applause_per_word']= captions_f['applause_total']/captions_f['wordcounts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.3 How many \"So\" and \"And so\", \"I think\" \"I don't think\" , \"--\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def countallso(text):\n",
    "    content = ';'.join(text)\n",
    "    num = len(re.findall(r'And so',content))\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = captions_f.text[934]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countallso(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countso = captions_f.text.apply(countallso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122ba5e90>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q4k9+6/jBU7ra79N7H2XnllUBPNjVjiVNGQZ/Dx0/eAonvvilvR6GpMJ0NPgjYgbweWAe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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a665310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countso.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['And_so_total'] = countso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([               u'id',              u'text',    u'laughter_total',\n",
       "                  u'length',          u'num_para',        u'wordcounts',\n",
       "       u'laughter_per_word',      u'word_per_sec',       u'para_length',\n",
       "          u'applause_total', u'applause_per_word',      u'And_so_total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129c35390>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9MZKQvzgiDs/MnwG7AUuHsMzsEWxHg2sxwJsrTULus+NsOCHfe/Q9GDglIp4FHgQO7HpV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\nJFXMkJekihnyklQxQ16SKvb/AW87HaZf96q3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124af43d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "captions_f.boxplot(column = 'And_so_total', by = 'neg_binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# captions_f['so_per_para'] = captions_f['so_total']/captions_f['num_para']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So seems to be not predictive after a few trial, move on to \"--\", which often stands for interjections in the transcripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a fun to find '--' and '-'\n",
    "def finddiv(text):\n",
    "    content = ';'.join(text)\n",
    "    num = len(re.findall(r'--',content))\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finddiv(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diversion = captions_f.text.apply(finddiv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['diversion'] = diversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge the response into captions_f "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f = captions_f.merge(response, on = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a383750>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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itu1dlyVJnRhvquc24Cg2LSX2wsy8tn58BXAYsB+wJjOfyMwH6/fs3YvOSpI6N2bwZ+al\nVNM3w9rXknwImAfsCDwwwvOSpD407hz/Zja2Pd4RuB94EJjb9vxcYP047awFFk1w371U/IrzI7Am\nTdakyZo09UtNRl30faLBf1NELMnMa4AjgdXAjcAZETEbmAM8nyrYx7J4gvvtpRZjFKhQ1qTJmjRZ\nk6aBqMmWBv/wEewkYGX95e2twCX1WT3nAtdRTR2tyMzHu99VSVI3DLVa/fKpZNoMxBF6ilmTJmvS\nZE2aBqImXsAlSYUx+CWpMAa/JBXG4Jekwhj8klQYg1+SCmPwS1JhDH5JKozBL0mFMfglqTAGvyQV\nxuCXpMIY/JJUGINfkgpj8EtSYQx+SSrMRJdeJCJmAecDz6Vag3c58BRwcf3zWuDEzCx+hRdJ6keT\nGfEfDuyQmQcBfwn8FXAO1ZKLB1OtPrOse12UJHXTZIL/EWBeRAwB84DHgRdl5rX161cAh3Wpf5Kk\nLpvwVA+wBpgD/DOwM7AUOLjt9Q1UBwRJUh+aTPCfDKzJzL+IiAXAVcA2ba/PBe4fp421wKJJ7LtX\n/D6iyZo0WZMma9LULzUZddH3yQT/DsCD9eP1dRs3RcSSzLwGOBJYPU4biyex315pMUaBCmVNmqxJ\nkzVpGoiaDLVaEzs4RcTTgYuAXahG+h8HfgKsBLYFbgWWD9BZPQPxDzXFrEmTNWmyJk0DUZMJB/8M\nNBD/UFPMmjRZkyZr0jQQNfECLkkqjMEvSYUx+CWpMAa/JBXG4Jekwhj8klQYg1+SCmPwS1JhDH5J\nKozBL0mFMfglqTAGvyQVxuCXpMIY/JJUGINfkgpj8EtSYSaz9CIR8WGqRda3AT5FtQD7xcBGqvV0\nTxygFbgkqSgTHvFHxCHASzPzQOAQ4PeBc4AVmXkw1eozy7rYR0lSF01mqudw4OaI+AZwOfBN4EWZ\neW39+hXAYV3qnySpyyYz1bMrsDvwR1Sj/cv53TUmNwDzOu+aJKkXJhP89wI/z8wngV9ExKPAs9te\nnwvcP04ba4FFk9h3r/h9RJM1abImTdakqV9qMuqi75OZ6rkeeBVARDwL2B5YHRFL6tePBK4d5b3D\nFted6oc/9EEf+u2PNbEm1mTwazKqoVZr4geniPifwKFUB44PA+uAlcC2wK3A8gE6q6fFOEUqkDVp\nsiZN1qRpIGoyqeCfYQbiH2qKWZMma9JkTZoGoiZewCVJhTH4JakwBr8kFcbgl6TCGPySVBiDX5IK\nY/BLUmEMfkkqjMEvSYWZ1EIsklSaoaGhs4Gjx9pm4cKF3H777evGaWpVq9X6UNc6NgmO+CWpMN6r\nZ0DurTHFrEmTNWmyJk0DURNH/JJUGINfkrpg6UmXrXv76VdOdze2iMEvSYXxrB5JDTPpDBY1TTr4\nI2I+8BPgFcBG4OL6v2uBEwdoBS5JEzTnaTsvuO+BR6e7G5qkSQV/RGwDfBZ4mOob7I8BKzLz2og4\nD1gGfKNrvZQ0pepR+qgj9aUnXbZu/k7bLbzg1MP3mLpeqVsmO8d/NnAe8Jv65xdm5vAC61cAh3Xa\nMUlSb0x4xB8RxwD3ZOaVEfFhmiu6bwDmdad7kjQYLj9n2R5U5/H3vclM9RwLtCLiMOAFwOeBXdte\nnwvcP04ba4FFk9j3FvvQhz7EqlWrtnTzMf+xjj76aM4+++zOOzVYBuIXeIpZk9r8nbYbfmhNmvql\nJqNeSNbRlbsRcRXwDqqpn3My85qI+AywOjO3OHV7YUvOSpjztJ0XbL311lttuP+u28dprrQzEwbi\n6sMpZk2arEnTQNSkG8F/AtVfdiWwLXArsHwQzupp+4Kq7/+hpthA/PJOMWvSZE2aBqIm3qtnQP6h\nppg1abImTdakaSBq4pW7klQYg1+SumCQ7tXjLRsKsiVfeIOX4ksznSN+SRM2SKNbNTniL8h4l+G3\nbwrs0dve9A9vSKbSFD3id9QiqUSO+FW8LfwkVNSnIM1sBr8kdcEg3aun6KkeNTn9Jc18jvilcdS3\n9uCCUw+f7q70jUEa3arJEb8kFaboEb+jFkklcsQvSYUx+CWpCwbpxIiip3rU5PSXNPMZ/NI4PBg2\neabTYJvMYuvbABcCC4HZwOnAz4GLgY1U6+meOAgrcElSiSYzx/9m4J7MPBh4FfBp4BxgRf3cELCs\ne13snUGak5OkbpnMVM8q4JL68SzgCeCFmXlt/dwVwOHANzrvniT13tKTLtuitSrGseDeBx5l6UmX\nretCl1Zdfs6ynt3pdcIj/sx8ODM3RMRcqoPAqZu1swGY16X+SdJUOBpY0GEbd+wyb043+rKAzg9C\nY5rUl7sRsTtwKfDpzPxyRPx128tzgfvHaWItsGgy++6m+TttN/zQ7yNqw1NfF5x6uDVpmhE1ufDy\nW1jz03/vqI1Zs4a494FHefvpV3Zck5ft82zetnR642A4Cy449fCFXWiuozbq/wcX0vnv26iLvk/m\ny91nAFcC78rMq+qnb4qIJZl5DXAksHqcZhZPdL+9cPf6R9bN32m7hYxRoNJYk6b6DJaFF5x6+Iyo\nydevvm0d1ajyjk7amb/TdgvvXv/I7R12Z8HXr77tjrctXbRHh+105O71j6yrH+7RYVMtOvx/p4t9\nGdVkRvwrqKZyTouI0+rn3gecGxHbArey6TuAnumzObmezsdJPXBHfZpqJzpeo6BL8+GaoAkHf2a+\njyroN3dIx72ZmOE5uU5GLXfsMm/OwrvXP9JJP4bn4wx+SQNh0C/gmvZRiyMWSYNm0INfbZz+krQl\nDP6ZxemvzXgwlJoM/pnH6a/f5cFQ2ozBrxJ4MJTaeD9+SSqMwS9JhTH4JakwBr8kFcbgl6TCDOxZ\nPa+854adnvPwr3ZYs+wL6zppZ/b8XXns7nsm3ca7tt5+wb/ssPvDA7L2THH8PWnql5pA/9SltJo4\n4pekwgzsiP97ux6w/nu7HrC+X87Pfm+HnVBv+HvS1C81gf6pS2k1GdjgV1O/fFztl4/vkkbmVI8k\nFcYR/wzSLx9X++Xju6SRdS34I2IW8DfA3sBjwHGZ+ctutT+ChdD5PVDm77Rd+1Jnk9HxEnbqKX9P\nmvqlJtA/dSmqJt2c6nktsG1mHgicApzTxbZ7ZcG9DzzaaRt3AKu60Bf1L39PmrpRE5hZdRmYmgy1\nWp0u5F6JiHOAGzLzq/XPd2Tmgq403iMzbRHtpSddNvyP2dEC2F1YRHsB3bkjZl+Yab8n3WBNmgap\nJt2c498ReLDt56ciYlZmbuziPrbY0NDQuAtwzHnazgu23nprLvzIXevGaW5Vq9Ua+Puor/3B53a6\n87Ybdhhrm6FZs7aCIVobnxrzoL3bngc8vPjlx68f5eWBGsWN97tS2u8JWJORzKSadHvE/8PMXFX/\n/KvM3L0rjUuSuqabc/xrgFcDRMRLgJ91sW1JUpd0c6rn68ArI2JN/fOxXWxbktQlXZvqkSQNBq/c\nlaTCGPySVBiDX5IKU+y9eqbhFhMDIyIOAM7KzEOnuy/TLSK2AS6kuqR/NnB6Zl4+vb2aXhGxFbAS\neC7VfZ3ekZm3TG+v+kNEzAd+ArwiM38x3f0ZTckj/kG8xUTPRcTJVP9Tz57uvvSJNwP3ZObBwKuA\nT01zf/rBHwEbM/Mg4FTgjGnuT1+oBwmfBR6e7r6Mp+TgfxnwHYDMvAF48fR2p2/cBhwF9P1l51Nk\nFXBa/XgW8OQ09qUvZOZlwAn1j3sAo13BXZqzgfOA30x3R8ZTcvCPeIuJ6epMv8jMSzHcfiszH87M\nDRExl+og8BfT3ad+kJlPRcTFwLnA301zd6ZdRBxD9cnwyvqpvh44lRx0DwJz236etvsKqb9FxO7A\nD4AvZOZXprs//SIzj6Ga518ZEdtNc3em27FUF7BeBbwA+HxEPGOa+zSqYr/cpbrFxFJglbeY0Gjq\n/3mvBN6VmVdNd3/6QUS8BViQmWcCjwAb6z/Fyswlw4/r8D8hM++axi6NqeTg9xYTY/OS7soKYB5w\nWkQMz/UfmZldufH6gLoEuDgirgG2Ad6XmY9Nc580Ad6yQZIKU/IcvyQVyeCXpMIY/JJUGINfkgpj\n8EtSYQx+SSqMwa9iRcQh9cU2o71+cUS8rlvtSf3C4JdG50UumpFKvnJXAiAilgCnA9sDOwEnZ+Yl\n9cuvj4gVVFeofjQzL63vR382sATYCrg4Mz8+DV2XJsURv0o2PKI/EXh7Zr4IOI5Nt2EeolqX4MVU\n9+I/NyJ2AZYDrXr7A4BlEXHQlPZc6oAjfpVs+Na5bwGWRsQbgJcAO9TPt4CLMrMF/DoibgBeChwG\n7BMRL6+32wFYDPx8ynoudcDgV8mGR/zXA6uBq+v/tt9f/qm2x0NUaxXMAj6Umd8AiIhdgYeoDhpS\n33OqRyUbAnYG9gT+W2Z+BziCat5++PU3AUTEQmA/4Aaqe/MfHxFb1wu0XAfsP8V9lybN4FfJWsB9\nwPnALfUtujcAsyNi+/r1xyLin4BvAsdn5n8AnwH+BbgJuBG4IDOvrbf3TCD1PW/LLEmFccQvSYUx\n+CWpMAa/JBXG4Jekwhj8klQYg1+SCmPwS1JhDH5JKsz/B76tqOAYUfJuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a733310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "captions_f.boxplot(column = 'diversion', by = 'label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "div_per_word = captions_f.diversion/captions_f.wordcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a21090>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IQr/WCweOcfyVyY73M/Al9bXXn3smf1Sc3+1qzKtv1/byy/2HO97PPnxJqggDX5IqomWX\nTkQMAncDFwLHgesz89mG8gK4BZgAtmfmtoayS4GvZ+a7yvW3ATuAKWAU+FRmTs/r0UiSmmrXwr8W\nWJmZG4EvArfPFETECmALcBUwAtwQEW8oyz4PbAVWNfyuLcDNmbkJGACuma+DkCS11y7wLwd2AmTm\nHuCShrILgH2ZOZaZ48ATwKaybB/wAerBPuMdmfl4ufwI8J7TrLskqQPtAv9s4FDD+mTZzTNTNtZQ\ndhhYA5CZ91Pv5mnUGP5HZraVJC2OdsMyDwFDDeuDmTlVLo+dUDYEvNjid001LA8BB+dQv1Hg7XPY\nbkEMDw/PLFb+uwbPxSzPxaylfi6Gh4c5eOB/d7saC2J4eHiEk5/3gZM8B7QP/N1AAdwXEZcBTzeU\n7QXWR8Ra4Cj17pzbWvyuJyNiJDMfA64GftDmtQGG22+ycEZHR3eVi1d2sRpLgudiludi1lI/F6Oj\no7ve9MahBb8QqhtGR0cfo8Pz3i7wHwCuiojd5frmiPggsDozt0bEZ4HvUe8aujcz95+wf+O7z58B\nWyNiJfBT4LudVFSSdHpaBn45bPITJzz9s4byh4GHm+z7C2Bjw/ozLNFWgCRVgRdeSVJFGPiSVBEG\nviRVhIEvSRVh4EtSRTgfvtRnvMuTmjHwpf6zYdngipGzzli74C/0yvhLAKxcccaCX9x09KUXmZwa\nX+iX6WsGvtSHzjpjLcPr/6Db1ZhXo888yqGjz3e7Gj3NPnxJqggDX5Iqwi4d9QW/qJTaM/DVLzas\nHBgYOXfZwv9JH5uqz/R95uDggn9ReWBygleml+TMw+pBBr76xrnLllOc81vdrsa8qh38NfsnHJmi\n+WHg9zC7MSR1oucC35B7jQ0DywdHlq9ZuWAVmjF1fBKAwVXLFrwbY2LsFaYnptpvKKkjPRf4wAYG\nV4wse905C/5CUxMvAzC4/HULHnKTLx+EU7ioZPmalZzz+7+zADXqnoM/+hXjB17udjWkvtOLgc+y\n153D6rde2e1qzKsjv9jF5LEXul0NSX3McfiSVBEGviRVhIEvSRVh4EtSRRj4klQRBr4kVYSBL0kV\n0XIcfkQMAncDFwLHgesz89mG8gK4BZgAtmfmtmb7RMRFQA14ptz9nsz8znwfkCTp5NpdeHUtsDIz\nN0bEpcDt5XNExApgC3AJcAzYHREPAVcAq06yz8XAlszcsjCHIklqpV2XzuXAToDM3EM93GdcAOzL\nzLHMHAeeADaV+zxykn0uBt4XEY9FxLaIWD1/hyFJaqdd4J8NHGpYnyy7bGbKxhrKDgNrmuyzDNgD\nfC4zR4CfA18+nYpLkjrTLvAPAUON22fmzDSGYyeUDQEHm+wzCTyYmU+Wzz0IXDSH+o0C042P4eHh\nBZ/IrFvKY5ue68Nz4bk42cNzUflz0VS7wN8NvBcgIi4Dnm4o2wusj4i1EbGSenfO37bY55GIeGe5\n/G7gx3M5JmCg8TE6OvrYHPbrSeWxDcz14bnwXJzs4bmo/Lloqt2Xtg8AV0XE7nJ9c0R8EFidmVsj\n4rPA96i/cdybmfsj4jf2KX/eCNwVEePAfuCGzg5P0hytO/rSi4w+82i36zGvjr70IsC6btejl7UM\n/MycBj5xwtM/ayh/GHh4DvuQmf9AfQSPJC2Wdc8fOMa3a3u7XY959fyBY3AKb349OR++pJaeO+uM\ntecPr/+DbtdjXo0+8yiHjj7/XLfr0csMfEn97Lk3nHvm+X9UnN/tesyrb9f28sv9hzt+8zPw1S/W\nHZiYoHbw192ux7w6MDEB9ltrnjiXjiRVhC189Yvnzl2+/PzinN/qdj3mVe3gr9k/MW6/teaFLXxJ\nqohebOGvm3z5IEd+savb9ZhXky8fBPtqJS0gW/iSVBG92MJ/btnrzjl/9Vuv7HY95tWRX+xi8tgL\nnfbVrpsYO87BH/1qQerULRNjx8FPO9K8s4UvSRXRiy18zXpu+ZpV55/z+7/T7XrMq4M/+hXjB152\nZIo0z2zhS1JFGPiSVBEGviRVhIEvSRVh4EtSRRj4klQRDsuU1NdeWKQ7Xh09Ng7AWWeuWPDXeqF+\nx6uOGfhSH1qse9q+Mv4SACtXnLHgr1Xe07ZTTx1/ZZJf7j8839U5mQ0AL469/NRivBjQ8esY+Oob\nByYX5wYox6amADhzcOF7RA9MTpzKbk9NTo1z6Ojz812dk9kA8PIrh5dkyNVqtc8sVEVOVBTFrvI1\nr1ys1+xUTwb+Ys2WOTXxMgCDy1+34K9VzpapU/fUK9PT7J8YX4zX2gAwNjVpyPVAyGlWLwb+U0yN\nM3nshcV4rQ0Ak0u09aJZhpzUXs8Fvv+xX2ti7JVFmS1z6vgkAIOrli34a02MvbLgryFVUcvAj4hB\n4G7gQuA4cH1mPttQXgC3ABPA9szc1myfiHgbsAOYAkaBT2Xm9PwfUqU8NT0xxfiBlxfjteqfdo6M\n+2lH6lHtWvjXAiszc2NEXArcXj5HRKwAtgCXAMeA3RHxEHAFsOok+2wBbs7MxyPiHuAa4MGFOKiq\n8NOOpE60G2ZwObATIDP3UA/3GRcA+zJzLDPHgSeATeU+j5xkn3dk5uPl8iPAe+blCCRJc9Iu8M8G\nDjWsT5ZdNjNlYw1lh4E1TfZZBgw0PHek3FaStEjadekcAoYa1gczc6pcHjuhbAg42GSfyYiYOsm2\ni6Yoig8DH+1wtw3lvrs63G97rVb7Zof7LBrPxSzPxSzPxax+PRcD09PNvzeNiA8ARWZujojLgFsy\n831l2QrgJ8ClwFHgb4EC+L2T7VP279+emY9FxF8CP8jM+xb06CRJr2oX+APMjrgB2AxcDKzOzK0R\n8X7gVupdQ/dm5j0n2yczfxYR64GtwErgp8DHHKUjSYunZeBLkvqH0yNLUkUY+JJUEQa+JFWEgS9J\nFdFzk6ctlnbzCFVROVXG1zPzXd2uS7eUw5G3A28BVgFfycxad2vVHeUFlVuB84Bp4MbM/El3a9U9\nEfEG4H8A787Mn3W7PidjC7+5V+cRAr5IfU6gyoqIz1P/z72q23Xpsj8GXsjMTcAfAn/R5fp00/uB\nqcy8AvgS8NUu16dryobAX1G/JmnJMvCbazWPUBXtAz7Aa6fIqKL7qF97AvX/P6d0S6p+kJn/Ffh4\nufpW4JTuQdgnbgPuAfZ3uyKtGPjNtZpHqHIy834qHG4zMvNoZh6JiCHq4f/vul2nbiqnTdkB3An8\nly5Xpysi4jrqn/pmbiK8ZBtFlQ2wOWg1j5AqLCLeDPw34JuZ+a1u16fbMvM66v34WyNi4e9mvvRs\nBq6KiB9Sn0/nGxGxrst1Oim/tG1uN/W5ge4r5wR6usv10RJQ/kd+FPhkZv6w2/Xppoj4EPCmzPwa\n8BL1mxtVrlGUmSMzy2Xofzwzn+tilZoy8Jt7gPq79u5yfXM3K7OEVH0ujpupT+19a0TM9OVfnZmL\nctuxJea7wI6IeAxYAXw6M493uU5qwbl0JKki7MOXpIow8CWpIgx8SaoIA1+SKsLAl6SKMPAlqSIM\nfKkUEVeWF840K98REf98vn6ftNgMfGnuvGhFPc0rbaUTRMQI8BXgTGAt8PnM/G5Z/C8i4mbqV5b+\n+8y8v5wX/jZgBFgG7MjMO7pQdaklW/jSrJkW/KeAP8nMi4HrmZ0OeYD6/QAuoT4X/p0R8Y+AjwHT\n5faXAtdExBWLWnNpDmzhS7NmprX9EFBExL8ELgPOKp+fBv46M6eB/xcRe4DfA94D/G5E/NNyu7OA\nYeB/LVrNpTkw8KVZMy38J4AfALvKn43zvE82LA9Qv0fAIPBvMvNBgIh4PXCY+puFtGTYpSPNGgDO\nBd4GfDkzdwL/jHq//Ez5vwKIiLcA7wT2UJ8b/4aIWF7eGOVHwD9Z5LpLbRn40qxp4ACwDfhJOTX2\nEWBVRJxZlh+PiP8JPATckJm/Bv4SeAZ4Evg74N7MfLzc3pE9WjKcHlmSKsIWviRVhIEvSRVh4EtS\nRRj4klQRBr4kVYSBL0kVYeBLUkUY+JJUEf8f+RsbWWI+5mgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a38ca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(div_per_word,groupby = captions_f.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['div_per_word'] = div_per_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "div_per_word_labeled = [col for colname,col in captions_f.groupby('label')['div_per_word']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value is:1.5824412727e-06\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = ss.f_oneway(*div_per_word_labeled)\n",
    "print 'p value is:{}'.format(p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How about \"I (don't) think\" \"my ** journey\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def findthink(text):\n",
    "    content = ';'.join(text)\n",
    "    num = len(re.findall(r'I\\s*[\\w\\']*\\s*think',content))\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test3 = captions_f.ix[643]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findthink(test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ithinks = captions_f.text.apply(findthink)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1535.000000\n",
       "mean        2.667101\n",
       "std         3.879449\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         1.000000\n",
       "75%         4.000000\n",
       "max        39.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ithinks.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1247d3610>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZBrVSrT39zLw1Ik466KWJg7b30psZMzRDvsUcxug286Iofh54XbX9srIsH2w4pMZUZRgu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\nL0kdYtKXpA75f9rvoe6n05UwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122e191d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(ithinks, groupby=captions_f.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions_f['ithinks'] = ithinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ithinks_labeled = [col for colname,col in captions_f.groupby('label')['ithinks']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value is:1.94337568156e-05\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = ss.f_oneway(*ithinks_labeled)\n",
    "print 'p value is:{}'.format(p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# (my) journey\n",
    "def findstory(text):\n",
    "    content = ';'.join(text)\n",
    "    story_words = '|'.join(['journey','story','experience'])\n",
    "    num = len(re.findall(story_words,content))\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "storywords = captions_f.text.apply(findstory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1535.000000\n",
       "mean        2.904235\n",
       "std         3.740518\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         2.000000\n",
       "75%         4.000000\n",
       "max        38.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storywords.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121cadad0>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Eg8CJeS915j0+DNR2hBsgy7LLgQRsz7Ls16uuR/XgedHjsWieQQ/kzr/JwFnAdB+fmaI7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\n696ZJaZ65QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d8bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(storywords, groupby=captions_f.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['storywords']=storywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2.2 Laughter or Applause as standalone paragraph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def laughpara(text):\n",
    "    counts = 0\n",
    "    for paras in text:\n",
    "        laugh = paras == '(Laughter)'\n",
    "        counts += laugh\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "laughpara = captions_f.text.apply(laughpara)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def applausepara(text):\n",
    "    counts = 0\n",
    "    for paras in text:\n",
    "        laugh = paras == '(Applause)'\n",
    "        counts += laugh\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "applausepara = captions_f.text.apply(applausepara)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions_f['app_as_para'] = applausepara\n",
    "captions_f['laugh_as_para'] = laughpara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([               u'id',              u'text',    u'laughter_total',\n",
       "                  u'length',          u'num_para',        u'wordcounts',\n",
       "       u'laughter_per_word',      u'word_per_sec',       u'para_length',\n",
       "          u'applause_total', u'applause_per_word',      u'And_so_total',\n",
       "               u'diversion',             u'label',      u'div_per_word',\n",
       "                 u'ithinks',        u'storywords',       u'app_as_para',\n",
       "           u'laugh_as_para'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talks_train = pd.read_json('train.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talks_train.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535, 31)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talks_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "talks_train.sort(columns = 'id',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "response.sort(columns = 'id',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "talks_train = talks_train.merge(response, on = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>laughter_total</th>\n",
       "      <th>length</th>\n",
       "      <th>num_para</th>\n",
       "      <th>wordcounts</th>\n",
       "      <th>laughter_per_word</th>\n",
       "      <th>word_per_sec</th>\n",
       "      <th>para_length</th>\n",
       "      <th>applause_total</th>\n",
       "      <th>applause_per_word</th>\n",
       "      <th>And_so_total</th>\n",
       "      <th>diversion</th>\n",
       "      <th>label</th>\n",
       "      <th>div_per_word</th>\n",
       "      <th>ithinks</th>\n",
       "      <th>storywords</th>\n",
       "      <th>app_as_para</th>\n",
       "      <th>laugh_as_para</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[Thank you so much, Chris. And it's truly a gr...</td>\n",
       "      <td>10</td>\n",
       "      <td>944.000</td>\n",
       "      <td>16</td>\n",
       "      <td>2139</td>\n",
       "      <td>0.004675</td>\n",
       "      <td>2.265890</td>\n",
       "      <td>133.687500</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001870</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>0.012155</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[In terms of invention,, I'd like to tell you ...</td>\n",
       "      <td>6</td>\n",
       "      <td>879.000</td>\n",
       "      <td>27</td>\n",
       "      <td>2604</td>\n",
       "      <td>0.002304</td>\n",
       "      <td>2.962457</td>\n",
       "      <td>96.444444</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2167</td>\n",
       "      <td>[When you grow up in a developing country\\nlik...</td>\n",
       "      <td>0</td>\n",
       "      <td>967.755</td>\n",
       "      <td>25</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.058372</td>\n",
       "      <td>79.680000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[A public, Dewey long ago observed,, is consti...</td>\n",
       "      <td>1</td>\n",
       "      <td>1099.000</td>\n",
       "      <td>22</td>\n",
       "      <td>2485</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>2.261146</td>\n",
       "      <td>112.954545</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012877</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99</td>\n",
       "      <td>[Okay., ♫ Strolling along in Central Park ♫, ♫...</td>\n",
       "      <td>2</td>\n",
       "      <td>131.000</td>\n",
       "      <td>28</td>\n",
       "      <td>173</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>1.320611</td>\n",
       "      <td>6.178571</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                               text  laughter_total  \\\n",
       "0     1  [Thank you so much, Chris. And it's truly a gr...              10   \n",
       "1     2  [In terms of invention,, I'd like to tell you ...               6   \n",
       "2  2167  [When you grow up in a developing country\\nlik...               0   \n",
       "3     3  [A public, Dewey long ago observed,, is consti...               1   \n",
       "4    99  [Okay., ♫ Strolling along in Central Park ♫, ♫...               2   \n",
       "\n",
       "     length  num_para  wordcounts  laughter_per_word  word_per_sec  \\\n",
       "0   944.000        16        2139           0.004675      2.265890   \n",
       "1   879.000        27        2604           0.002304      2.962457   \n",
       "2   967.755        25        1992           0.000000      2.058372   \n",
       "3  1099.000        22        2485           0.000402      2.261146   \n",
       "4   131.000        28         173           0.011561      1.320611   \n",
       "\n",
       "   para_length  applause_total  applause_per_word  And_so_total  diversion  \\\n",
       "0   133.687500               4           0.001870             1         26   \n",
       "1    96.444444               1           0.000384             4         14   \n",
       "2    79.680000               1           0.000502             0          2   \n",
       "3   112.954545               1           0.000402             0         32   \n",
       "4     6.178571               2           0.011561             1          0   \n",
       "\n",
       "   label  div_per_word  ithinks  storywords  app_as_para  laugh_as_para  \n",
       "0      4      0.012155        1           6            2              2  \n",
       "1      1      0.005376        9           0            1              5  \n",
       "2      0      0.001004        0           0            1              0  \n",
       "3      0      0.012877        3           5            1              1  \n",
       "4      4      0.000000        0           0            1              0  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['funny_binary']= captions_f.label == 4\n",
    "captions_f['neg_binary'] = captions_f.label == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1237ce4d0>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W61jQbqXqSIMR/rhSD3Kf7bK+RmPkN9WImAGcA8yvJr0V+C2wCpgJrAdO8OyaSeE/jKYa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\neiHBKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1237ce750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "captions_f.boxplot(column='laugh_as_para',by = 'funny_binary',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['app_total_log'] = captions_f['applause_total'].apply(lambda x: np.log10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12421a810>"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124214e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "captions_f.boxplot(column='applause_total',by = 'neg_binary',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([               u'id',              u'text',    u'laughter_total',\n",
       "                  u'length',          u'num_para',        u'wordcounts',\n",
       "       u'laughter_per_word',      u'word_per_sec',       u'para_length',\n",
       "          u'applause_total', u'applause_per_word',      u'And_so_total',\n",
       "               u'diversion',             u'label',      u'div_per_word',\n",
       "                 u'ithinks',        u'storywords',       u'app_as_para',\n",
       "           u'laugh_as_para',      u'funny_binary',        u'neg_binary',\n",
       "           u'app_total_log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179.000000\n",
       "mean       1.189944\n",
       "std        0.952648\n",
       "min        0.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        4.000000\n",
       "Name: app_as_para, dtype: float64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f[captions_f.neg_binary==True].app_as_para.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1356.000000\n",
       "mean        1.889381\n",
       "std         1.814946\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max        17.000000\n",
       "Name: app_as_para, dtype: float64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f[captions_f.neg_binary==False].app_as_para.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Save captions_f "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([               u'id',              u'text',    u'laughter_total',\n",
       "                  u'length',          u'num_para',        u'wordcounts',\n",
       "       u'laughter_per_word',      u'word_per_sec',       u'para_length',\n",
       "          u'applause_total', u'applause_per_word',      u'And_so_total',\n",
       "               u'diversion',             u'label',      u'div_per_word',\n",
       "                 u'ithinks',        u'storywords',       u'app_as_para',\n",
       "           u'laugh_as_para',      u'funny_binary',        u'neg_binary',\n",
       "           u'app_total_log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f.to_json('captions_f.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###2.3 Mining transcripts with spaCy and NLTK "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import unicode_literals\n",
    "from spacy.en import English, attrs\n",
    "from spacy import en\n",
    "from nltk import word_tokenize\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from spacy.parts_of_speech import ADV, NOUN, ADJ, PUNCT, VERB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove punctuations\n",
    "from string import punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nlp = English()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions_f = pd.read_json('captions_f.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions_f.sort(columns = 'id',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535, 22)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### combine all paragraphs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "punset = set(punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stopset = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "to keep it's that's etc unseparated, do this\n",
    "test3 = re.sub('\\'','\\u2019',test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2.3.1 Tokenize the text, lower the case, remove punctuations and interjections (laughter, mob, applause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tokenize(text):\n",
    "    # return word tokens\n",
    "    # text comes in as list of paragraph\n",
    "    text = ' '.join(text)\n",
    "    # remove '(Laughter)' etc, \n",
    "    text = re.sub('\\([\\w\\s]+\\)','',text)\n",
    "    text = text.lower()\n",
    "    tokens = nlp(text, parse = False)\n",
    "    words = []\n",
    "    for token in tokens:\n",
    "        # remove punctuations\n",
    "        if token.pos != PUNCT:\n",
    "            words.append(token)\n",
    "    return words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2.3.2 Count stopwords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stopcounts(token_list):\n",
    "    # token_list comes in as a list\n",
    "    num_words = len(token_list)\n",
    "    count = 0\n",
    "    for token in token_list:\n",
    "        if token.string.strip(' ').lower() in stopset:\n",
    "            count += 1\n",
    "    return count/float(num_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tokens = captions_f.text.apply(tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['cap_tokens'] = tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stopwords_ratio = captions_f['cap_tokens'].apply(stopcounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168642150>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VHTVk5kvA2mn22QXs6mFtkgqzZHQZF19yWcf9Toy/Ng/VnDt8YEqSCmfQS1LhDHpJKpxB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\ng16SCmfQS1LhDHpJKtz/Be7XYY4AzEzSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bd9a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopwords_ratio.hist(bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['stopwords_ratio'] = stopwords_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([     u'And_so_total',       u'app_as_para',     u'app_total_log',\n",
       "       u'applause_per_word',    u'applause_total',      u'div_per_word',\n",
       "               u'diversion',      u'funny_binary',                u'id',\n",
       "                 u'ithinks',             u'label',     u'laugh_as_para',\n",
       "       u'laughter_per_word',    u'laughter_total',            u'length',\n",
       "              u'neg_binary',          u'num_para',       u'para_length',\n",
       "              u'storywords',              u'text',      u'word_per_sec',\n",
       "              u'wordcounts',        u'cap_tokens',   u'stopwords_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168855110>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x168861050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(stopwords_ratio,groupby = captions_f.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stopwords_labeled = [col for colname,col in captions_f.groupby('label')['stopwords_ratio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value is:0.361236442369\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = ss.f_oneway(*stopwords_labeled)\n",
    "print 'p value is:{}'.format(p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####So there is no evident difference  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.2 Count important POS, take label class 2 (jaw-dropping) and 0 (informative) for example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the most jaw-dropping but not informative talks(3) and the reversed 3 talks, look at the noun, adj and adv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jawids = [129, 65, 199]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infoids = [152, 899, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of tokens (verbs and nouns) are estimated based on counts from a 3 billion word corpus, smoothed using the simple good-turing methods. token.prob outputs the log prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a function to take cap_tokens as input and output list of unique tokens \n",
    "# joined as a string for CountVectorizer \n",
    "def getpos(token_list,prob = -10,pos = ADJ):\n",
    "    words = []\n",
    "    for token in token_list:\n",
    "        if token.pos == pos and token.prob < prob:\n",
    "            words.append(token.lemma_)\n",
    "            words = list(set(words))\n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['noun_token'] = captions_f.cap_tokens.apply(getpos,pos = NOUN, prob = -3).apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f['verb_token'] = captions_f.cap_tokens.apply(getpos,pos = VERB, prob = -3).apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "noungroupby = captions_f.groupby('label')['noun_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "verbgroupby = captions_f.groupby('label')['verb_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define a function to receive groupby obj and output dict(ind: most common vocab)\n",
    "def sharedpos(groupbyobj,mindf, maxfeature):\n",
    "    sharedtoken = dict()\n",
    "    vect = CountVectorizer(min_df = mindf, binary = True, max_features = maxfeature)\n",
    "    for ind, tokengb in groupbyobj:\n",
    "        print ind, tokengb.as_matrix().shape\n",
    "        vect.fit(tokengb.as_matrix())\n",
    "        sharedtoken[ind]=set(vect.vocabulary_)\n",
    "    return sharedtoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (474,)\n",
      "1 (418,)\n",
      "2 (353,)\n",
      "3 (179,)\n",
      "4 (111,)\n"
     ]
    }
   ],
   "source": [
    "sharednoun = sharedpos(noungroupby,0.6,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (474,)\n",
      "1 (418,)\n",
      "2 (353,)\n",
      "3 (179,)\n",
      "4 (111,)\n"
     ]
    }
   ],
   "source": [
    "sharedverb = sharedpos(verbgroupby,0.6, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "common_noun = sharednoun[0]&sharednoun[1]&sharednoun[2]&sharednoun[3]&sharednoun[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vocab_noun = []\n",
    "for value in sharednoun.itervalues():\n",
    "    vocab_noun.extend(list(value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vocab_noun_final = list(set(vocab_noun) - common_noun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'life',\n",
       " u'percent',\n",
       " u'question',\n",
       " u'story',\n",
       " u'idea',\n",
       " u'part',\n",
       " u'place',\n",
       " u'fact',\n",
       " u'example',\n",
       " u'problem',\n",
       " u'bit',\n",
       " u'kind',\n",
       " u'today']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_noun_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "common_verb = sharedverb[0]&sharedverb[1]&sharedverb[2]&sharedverb[3]&sharedverb[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab_verb = []\n",
    "for value in sharedverb.itervalues():\n",
    "    vocab_verb.extend(list(value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab_verb_final = list(set(vocab_verb) - common_verb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'like',\n",
       " u'show',\n",
       " u'feel',\n",
       " u'should',\n",
       " u'leave',\n",
       " u'live',\n",
       " u'understand',\n",
       " u'talk',\n",
       " u'ask',\n",
       " u'become',\n",
       " u'change']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_verb_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "captions_f.drop('cap_tokens',axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([     u'And_so_total',       u'app_as_para',     u'app_total_log',\n",
       "       u'applause_per_word',    u'applause_total',      u'div_per_word',\n",
       "               u'diversion',      u'funny_binary',                u'id',\n",
       "                 u'ithinks',             u'label',     u'laugh_as_para',\n",
       "       u'laughter_per_word',    u'laughter_total',            u'length',\n",
       "              u'neg_binary',          u'num_para',       u'para_length',\n",
       "              u'storywords',              u'text',      u'word_per_sec',\n",
       "              u'wordcounts',   u'stopwords_ratio',        u'noun_token',\n",
       "              u'verb_token'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "captions_f.to_json('captions_f.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nlp = English()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sculptor = nlp.vocab[u'sculptor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-13.12763500213623"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sculptor.prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.15213096, -0.00937699,  0.00855101, -0.05223943, -0.01507325], dtype=float32)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sculptor.repvec[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "words = [w for w in nlp.vocab if w.has_repvec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "words.sort(key=lambda w: cosine(w.repvec, sculptor.repvec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-20 sculptor, sculptor, sculptor, painter, painter, painter, printmaker, architect, architect, architect, muralist, portraitist, miniaturist, caricaturist, illustrator, illustrator, engraver, dramatist, dramatist, ceramicist\n"
     ]
    }
   ],
   "source": [
    "print '1-20', ', '.join(w.lower_ for w in words[0:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice lemma is not defined for pronouns in WordNet, therefore the author makes up the policy;\n",
    "Tag vs Pos: Pos (part-of-speech, from google universal tag set); Tag: a fine-grained morphosyntactic tag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Test for instance, the distance between \"sculptor\" and \"design, art, science, technology, global\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testwords = ['design','science','artist','technology','global','painter','scientist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "design 0.628889799118\n",
      "science 0.720556050539\n",
      "artist 0.276574373245\n",
      "technology 0.750671461225\n",
      "global 0.859910815954\n",
      "painter 0.129379868507\n",
      "scientist 0.412072837353\n"
     ]
    }
   ],
   "source": [
    "for word in testwords:\n",
    "    print word,cosine(nlp.vocab[word].repvec,sculptor.repvec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.vocab['good']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gore's talk has 194 paragraphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def countpos(text):\n",
    "    posdict = dict()\n",
    "    tokens = nlp(text,parse = False)\n",
    "    for token in tokens:\n",
    "        if token.pos_ in posdict.keys():\n",
    "            posdict[token.pos_]+=1\n",
    "        else:\n",
    "            posdict[token.pos_]=1\n",
    "    return posdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "posdict = countpos(test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'ADJ': 1,\n",
       " u'ADV': 3,\n",
       " u'CONJ': 1,\n",
       " u'DET': 1,\n",
       " u'NOUN': 3,\n",
       " u'PRON': 2,\n",
       " u'PUNCT': 2,\n",
       " u'VERB': 1}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tokens = nlp(test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1L: 1, 2L: 3, 4L: 1, 5L: 1, 6L: 3, 8L: 2, 10L: 1, 12L: 2}"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens.count_by(attrs.POS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrs.ORTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thank -12.0132427216\n",
      "you -6.68225240707\n",
      "so -7.10154438019\n",
      "much -7.50383710861\n",
      ", -3.03683543205\n",
      "Chris -10.0064516068\n",
      ". -3.17597317696\n",
      "And -7.47497653961\n",
      "it -5.49812936783\n",
      "'s -4.73858451843\n",
      "truly -10.7227811813\n",
      "a -4.00384140015\n",
      "great -8.39175319672\n",
      "honor -10.3253259659\n"
     ]
    }
   ],
   "source": [
    "for token in tokens:\n",
    "    print token.orth_, token.prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6.227705478668213"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.vocab[u'this'].prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "truly = tokens[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truly.repvec.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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