2. Topic Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> \n",
    "        <h1>Natural Language Processing For Everyone</h1>\n",
    "        <h1>Topic Modeling</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "        @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "import gzip\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from numpy.linalg import svd\n",
    "from numpy import diag\n",
    "\n",
    "import watermark\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List out the versions of all loaded libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "watermark 2.0.2\n",
      "json      2.0.9\n",
      "autopep8  1.5\n",
      "numpy     1.18.1\n",
      "Mon Sep 14 2020 \n",
      "\n",
      "CPython 3.7.3\n",
      "IPython 6.2.1\n",
      "\n",
      "compiler   : Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 19.6.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n",
      "Git hash   : 2746551f5f81e82704a3a31f15d48dc15b562707\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the default style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a standard list of stopwords from a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = set([word.lower().strip() for word in open(\"data/nltk_stopwords.txt\", \"rt\").readlines()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['below',\n",
       " 'himself',\n",
       " 'll',\n",
       " 'd',\n",
       " \"that'll\",\n",
       " 'more',\n",
       " 'not',\n",
       " 'couldn',\n",
       " 'same',\n",
       " 'between']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(stopwords)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to extract the words from our input file. Here we have to be (slightly) more careful than in the previous lesson since we want to keep #tags and @mentions and remove stopwords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_words(text, stopwords):\n",
    "    temp = text.split() # Split the text on whitespace\n",
    "    text_words = []\n",
    "\n",
    "    punctuation = set(string.punctuation)\n",
    "    \n",
    "    #Keep #tags and @mentions\n",
    "    punctuation.remove(\"#\")\n",
    "    punctuation.remove(\"@\")\n",
    "    \n",
    "    for word in temp:\n",
    "        # Remove any punctuation characters present in the beginning of the word\n",
    "        while len(word) > 0 and word[0] in punctuation:\n",
    "            word = word[1:]\n",
    "\n",
    "        # Remove any punctuation characters present in the end of the word\n",
    "        while len(word) > 0 and word[-1] in punctuation:\n",
    "            word = word[:-1]\n",
    "\n",
    "        # Simple rule to eliminate (most) URLs \"http://www.google.com\"\n",
    "        if len(word) > 0 and \"/\" not in word:\n",
    "            # If it's not a stopword\n",
    "            if word.lower() not in stopwords:\n",
    "                # Append this word into our list of words.\n",
    "                text_words.append(word.lower())\n",
    "        \n",
    "    return text_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process the data from the file. File was obtained from crowdflower (https://www.crowdflower.com/data-for-everyone/) and contains a list of tweets about Apple from 2014 and some information about their sentiment. For our purposes, we only care about the text of each tweet that is located in the last column of the csv file.\n",
    "\n",
    "We treat each tweet as an individual document, and limit ourselves to only the first 200 tweets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = []\n",
    "line_count = 0\n",
    "\n",
    "for line in open(\"data/Apple-Twitter-Sentiment-DFE.csv\", \"rt\"):\n",
    "    fields = line.strip().split(',')\n",
    "    \n",
    "    line_count += 1\n",
    "    \n",
    "    # Skip the first line of the file which contains the header\n",
    "    if line_count == 1:\n",
    "        continue\n",
    "    \n",
    "    text = \",\".join(fields[11:])\n",
    "    \n",
    "    if len(text) == 0:\n",
    "        continue\n",
    "    \n",
    "    words = extract_words(text, stopwords)\n",
    "    \n",
    "    if len(words) > 0:\n",
    "        tweets.append(words)\n",
    "        \n",
    "    if len(tweets) == 200:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['cat', 'chews', '@apple', 'cords', '#applesnob'],\n",
       " ['nobody', 'expects', 'spanish', 'inquisition', '#aapl']]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the function to calculate the Inverse Document Frequency for each word and the TFIDF matrix. These functions were introduced in the previous lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inv_doc_freq(corpus_words):\n",
    "    number_docs = len(corpus_words)\n",
    "    \n",
    "    document_count = {}\n",
    "\n",
    "    for document in corpus_words:\n",
    "        word_set = set(document)\n",
    "\n",
    "        for word in word_set:\n",
    "            document_count[word] = document_count.get(word, 0) + 1\n",
    "    \n",
    "    IDF = {}\n",
    "    \n",
    "    for word in document_count:\n",
    "        IDF[word] = np.log(number_docs/document_count[word])\n",
    "        \n",
    "    \n",
    "    return IDF\n",
    "\n",
    "def tf_idf(corpus_words):\n",
    "    IDF = inv_doc_freq(corpus_words)\n",
    "    \n",
    "    TFIDF = []\n",
    "    \n",
    "    for document in corpus_words:\n",
    "        TFIDF.append(Counter(document))\n",
    "    \n",
    "    for document in TFIDF:\n",
    "        for word in document:\n",
    "            document[word] = document[word]*IDF[word]\n",
    "            \n",
    "    return TFIDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the TFIDF matrix for our corpus of tweets. We should note that while we call it a matrix, this is effectively a list of dictionaries, which we can consider to be a sparse representation of a matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "TFIDF = tf_idf(tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Counter({'#applesnob': 5.298317366548036,\n",
       "          '@apple': 0.2169130015635736,\n",
       "          'cat': 5.298317366548036,\n",
       "          'chews': 5.298317366548036,\n",
       "          'cords': 5.298317366548036}),\n",
       " Counter({'#aapl': 1.9310215365615626,\n",
       "          'expects': 5.298317366548036,\n",
       "          'inquisition': 5.298317366548036,\n",
       "          'nobody': 5.298317366548036,\n",
       "          'spanish': 5.298317366548036}),\n",
       " Counter({'#aapl:5': 5.298317366548036,\n",
       "          'apple': 2.5902671654458267,\n",
       "          'buy': 3.506557897319982,\n",
       "          'december': 4.605170185988092,\n",
       "          'gains': 5.298317366548036,\n",
       "          'rocket': 5.298317366548036,\n",
       "          'stocks': 5.298317366548036})]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, extract the word_dict and word_list from the TFIDF matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_vocabulary(TFIDF):\n",
    "    words = set()\n",
    "    \n",
    "    for document in TFIDF:\n",
    "        words |= set(document.keys())\n",
    "    \n",
    "    word_list = list(words)\n",
    "    word_dict = dict(zip(word_list, range(len(word_list))))\n",
    "    \n",
    "    return word_dict, word_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_dict, word_list = build_vocabulary(TFIDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 927 words in our vocabulary\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = len(word_dict)\n",
    "print(\"We have\", vocabulary_size, \"words in our vocabulary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['powers',\n",
       " 'york',\n",
       " '#recent',\n",
       " 'chart',\n",
       " 'boot',\n",
       " 'buying',\n",
       " 'amendment',\n",
       " \"that's\",\n",
       " 'people',\n",
       " '#iphone']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_list[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use the TFIDF matrix and our vocabulary to generate the Term Document matrix. This is just a matter of rearranging the values in our (sparse) TFIDF matrix into the full TD matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def term_document_matrix(TFIDF, word_list, word_dict):\n",
    "    vocabulary_size = len(word_dict)\n",
    "    number_documents = len(TFIDF)\n",
    "    \n",
    "    TDM = np.zeros((vocabulary_size, number_documents))\n",
    "    \n",
    "    for doc in range(number_documents):\n",
    "        document = TFIDF[doc]\n",
    "        \n",
    "        for word in document.keys():\n",
    "            pos = word_dict[word]\n",
    "            \n",
    "            TDM[pos, doc] = document[word]\n",
    "            \n",
    "    return TDM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our dataset has:\n",
      "927 unique words\n",
      "200 documents\n"
     ]
    }
   ],
   "source": [
    "TDM = term_document_matrix(TFIDF, word_list, word_dict)\n",
    "print(\"Our dataset has:\\n%u unique words\\n%u documents\"%(TDM.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(927, 200)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TDM.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicit Semantic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In ESA we use the TD matrix of our corpus as a knowledge base that we can use to look up related documents. To demonstrate how this works, let's create a new document:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_tweet = ['#macbook', 'mini', 'rocket']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not an actual sentence since, for clarity, we are not including stopwords.\n",
    "\n",
    "In the TD formulation, the values in the word (row) vector representa how much each document is \"activated\" by that specific word. Naturally, documents in which the word does not appear have weight zero. We can use these word vectors to find a vector representation of our new document where each value is our related it is to every other document in our knowledge base. You can think of this as the set of results one might obtain after a query in a search engine!\n",
    "\n",
    "The **find_related** simply calcuates the vector corresponding to the new \"document\" and returns a list of the corresponding weights sorted in decreasing order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_related_docs(tweet, TDM):\n",
    "    new_vector = np.zeros(TDM.shape[1])\n",
    "    \n",
    "    for word in tweet:\n",
    "        pos = word_dict[word]\n",
    "        new_vector += TDM[pos, :]\n",
    "        \n",
    "    # Now the entries of new_vector tell us which documents are activated by this one.\n",
    "    # Let's extract the list of documents sorted by activation\n",
    "    doc_list = sorted(zip(range(TDM.shape[1]), new_vector), key=lambda x:x[1], reverse=True)\n",
    "    \n",
    "    return doc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "related = find_related_docs(new_tweet, TDM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look a the top 5 results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.607650686531799 166 ipad mini unboxing via @youtube @apple #ipadmini #ipad #macbook #macbookpro #startup #hipster #unboxing\n",
      "6.607650686531799 167 ipad mini first time startup via @youtube @apple #ipadmini #ipad #macbook #macbookpro #startup #hipster #unbox\n",
      "6.607650686531799 190 ipad mini unboxing via @youtube @apple #ipadmini #ipad #macbook #macbookpro #startup #hipster #unboxing\n",
      "6.607650686531799 191 ipad mini first time startup via @youtube @apple #ipadmini #ipad #macbook #macbookpro #startup #hipster #unbox\n",
      "5.298317366548036 2 #aapl:5 rocket stocks buy december gains apple\n"
     ]
    }
   ],
   "source": [
    "for tweet, score in related[:5]:\n",
    "    print(score, tweet, \" \".join(tweets[tweet]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With such small documents, the results are easy to interpret. The first two result are due to mentions of **macbook** and **mini**, the third due to the presence of the word **rocket** but the remaining two results don't include any words in common with our original tweet, just words that appear in similar contexts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(vec1, vec2):\n",
    "    sim = np.dot(vec1, vec2)\n",
    "    norm1 = np.sqrt(np.dot(vec1, vec1))\n",
    "    norm2 = np.sqrt(np.dot(vec2, vec2))\n",
    "    \n",
    "    return sim/(norm1*norm2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_words(tweet, TDM):\n",
    "    new_vector = np.zeros(TDM.shape[1])\n",
    "    \n",
    "    for word in tweet:\n",
    "        pos = word_dict[word]\n",
    "        new_vector += TDM[pos, :]\n",
    "    \n",
    "    sim = [similarity(new_vector, TDM[i, :]) for i in range(TDM.shape[0])]\n",
    "    \n",
    "    sim_words = sorted(zip(range(TDM.shape[0]), sim), key=lambda x:x[1], reverse=True)\n",
    "    \n",
    "    return sim_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "similar = find_similar_words(new_tweet, TDM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#macbook 0.842346298979266\n",
      "#hipster 0.8273747381258668\n",
      "@youtube 0.8273747381258668\n",
      "#startup 0.8273747381258668\n",
      "mini 0.8227410508913036\n",
      "#ipadmini 0.7963450852368475\n",
      "#macbookpro 0.7800563838816592\n",
      "ipad 0.7617102018231607\n",
      "via 0.7055875474518816\n",
      "#ipad 0.6502130393344844\n"
     ]
    }
   ],
   "source": [
    "for word, score in similar[:10]:\n",
    "    print(word_list[word], score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This approach also returns words that are not included in the document but that are used in a similar context, such as #hipster. One might use this extra words (above some finetuned threshold of similarity) to expand our query and retrieve furhter related documents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latent Semantic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSA analysis, sometimes refered to as Latent Semantic Indexing, relies on the well known Singular Value Decomposition. Singular Values (and Singular Vectors) can be thought of as being a generalization of the more common eigenvalues (and eigenvectors) for the case of non-square matrices. \n",
    "\n",
    "The first step in any LSA analysis is to apply SVD to the Term-Document matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "u, sigma, vt = svd(TDM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting matrices have the expected shapes, except the singular value matrix, **sigma** that is simply a vector. This is an optimization of the numpy function meant to save memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(927, 927) (200,) (200, 200)\n"
     ]
    }
   ],
   "source": [
    "m, n = TDM.shape\n",
    "print(u.shape, sigma.shape, vt.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "927 200\n"
     ]
    }
   ],
   "source": [
    "print(m, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now reduce the internal dimention of these matrices to, say, 10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 10\n",
    "\n",
    "# Convert the vector of singular values into a diagonal matrix\n",
    "sigma_k = sigma[:k]\n",
    "Sk = diag(sigma_k)\n",
    "\n",
    "# Drop the extraneous dimensions in the other two matrices.\n",
    "uk = u[:,:k]\n",
    "vtk = vt[:k, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this way, we preserve the most significant (higher sigma values) latent dimensions of the dataset while discarding the rest. The idea is simple, SVD projects the original dataset into an internal space which is a linear combination of the original one. Each dimension in this space can be thought of as an underlying topic in the data. By preserving only the most dominant topics in effect we are simplifying the data and, potentially, removing noise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(927, 10) (10, 10) (10, 200)\n"
     ]
    }
   ],
   "source": [
    "print(uk.shape, Sk.shape, vtk.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no easy ways to determine the number of topics to choose, but a good rule of thumb is to identify kinks in the values of sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faf8e2310b8>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(200), sigma, '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also explore the way in which the topics are defined by considering the words for which they have stronger weights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the list of words by the weight they have in a specific topic\n",
    "def top_words(vector, word_list):\n",
    "    doc_list = sorted(zip(word_list, vector), key=lambda x:x[1], reverse=True)\n",
    "    \n",
    "    return doc_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, for topic 3, we have strong mentions of AIDS day and the Produc (RED) iPhone launch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day 0.28746488014330057\n",
      "aids 0.2826216409753877\n",
      "world 0.2692253682039401\n",
      "red 0.26475561492615646\n",
      "logo 0.22903406070460355\n",
      "#apple 0.18504347302677782\n",
      "apple 0.13868223906604044\n",
      "abl 0.12638896410764222\n",
      "dec 0.12638896410764222\n",
      "launches 0.12638896410764217\n"
     ]
    }
   ],
   "source": [
    "topic_words = top_words(uk[:,3], word_list)\n",
    "for word, weight in topic_words[:10]:\n",
    "    print(word, weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, if we were trying to map a specific sentence or document into the most relevant topic we would simply have to project it into the singular space. First we define the vector in the \"regular\" word space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "document = ['ipad', 'mini', 'price']\n",
    "\n",
    "doc_vector = np.zeros(vocabulary_size)\n",
    "for word in document:\n",
    "    doc_vector[word_dict[word]] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and now we project it into the singular space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([ 1.62243400e-04,  1.44900491e-02, -1.09973213e-03,  4.30257980e-05,\n",
      "        9.69106965e-04, -4.54646796e-03,  7.58613138e-04, -2.40772326e-03,\n",
      "        3.82599561e-03, -1.95924079e-03])\n"
     ]
    }
   ],
   "source": [
    "doc_singular = 1/sigma_k*np.dot(uk.T, doc_vector)\n",
    "pprint(doc_singular)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From where we see that hte most relevant topic is topic number 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "#macbook 0.30723559382139015\n",
      "@youtube 0.30540145699726684\n",
      "#startup 0.30540145699726673\n",
      "#hipster 0.3054014569972667\n",
      "#macbookpro 0.2980960628908025\n",
      "via 0.2918795721098776\n",
      "mini 0.20949666233803643\n",
      "ipad 0.2038176600121253\n",
      "#ipadmini 0.19891025664321735\n",
      "startup 0.1932748800761492\n"
     ]
    }
   ],
   "source": [
    "topic = np.argmax(doc_singular)\n",
    "\n",
    "print(topic)\n",
    "\n",
    "topic_words = top_words(uk[:,topic], word_list)\n",
    "for word, weight in topic_words[:10]:\n",
    "    print(word, weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which indeed seems relevant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-Negative Matrix Factorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea behind applying NMF to a Term-Document Matrix is similar to the goals behind using SVD to perform Latent Semantic Analysis. NMF is a general matrix factorization algorithm and, as such, the factor matrices it finds can also be thought of as internal or latent dimensions. \n",
    "\n",
    "Similarly to the case of SVD, we can perform a partial reconstruction of the original matrix by reducing the internal dimensions. Naturally, as the number of latent dimensions increases, the reconstruction error decreases since we are removing less and less information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NMF(V, k):\n",
    "    n, m = V.shape\n",
    "\n",
    "    W = np.random.rand(n, k)\n",
    "    H = np.random.rand(k, m)\n",
    "\n",
    "    error = 1\n",
    "    err = 1\n",
    "\n",
    "    while err > 1e-3:\n",
    "        old_error = error\n",
    "\n",
    "        hn = np.dot(W.T, V)\n",
    "        hd = np.dot(W.T, np.dot(W, H))\n",
    "\n",
    "        H = H*np.divide(hn, hd)\n",
    "\n",
    "        wn = np.dot(V, H.T)\n",
    "        wd = np.dot(W, np.dot(H, H.T))\n",
    "\n",
    "        W = W*np.divide(wn, wd)\n",
    "\n",
    "        error = np.sum(np.power(V-np.dot(W, H), 2.0))\n",
    "        err = np.abs(old_error-error)\n",
    "\n",
    "    return W, H, error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the error changes with the number of components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 32018.682027744457\n",
      "3 31373.942968079726\n",
      "4 30835.315902309783\n",
      "5 30409.302157133076\n",
      "6 29821.02337795089\n",
      "7 29360.333361800775\n",
      "8 28894.47843266444\n",
      "9 28464.630632822347\n",
      "10 28055.56263544962\n",
      "11 27698.4240208882\n",
      "12 27209.911674699084\n",
      "13 26889.016796463704\n",
      "14 26433.470410179318\n",
      "15 26017.25854048358\n",
      "16 25678.18979451143\n",
      "17 25367.76547377013\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bgoncalves/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:21: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/Users/bgoncalves/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide\n",
      "/Users/bgoncalves/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in multiply\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 nan\n",
      "19 24507.235094574695\n",
      "20 24172.18159357293\n",
      "21 23790.316718548533\n",
      "22 23417.434557622404\n",
      "23 23265.915794360695\n",
      "24 22702.40443021297\n",
      "25 22466.414424116712\n"
     ]
    }
   ],
   "source": [
    "error = []\n",
    "\n",
    "for comps in range(2, 25+1):\n",
    "    W, H, err = NMF(TDM, comps)\n",
    "    print(comps, err)\n",
    "    error.append(err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The nan values found above are due to numerical instabilities. In practice you should use a well tested numerical library such as sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Non-Negative Matrix Factorization')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2, 25+1), error)\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('reconstruction error')\n",
    "plt.title('Non-Negative Matrix Factorization')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the remainder of this section, lets fix the number of so that we can use these results to illustrate the power of NMF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "W, H, err = NMF(TDM, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we apply NMF to a Term-Document matrix, as defined above, the resulting factors prove useful for topic detection. In particular, the W matrix provides us with the definition of each topic as a weighted distribution over all the words in the corpus:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a couple of examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#market 14.899029233952366\n",
      "#recent 14.713704778286651\n",
      "sold 14.713704778286651\n",
      "#forex 14.713704778286651\n",
      "20 14.713704778286651\n",
      "#nasdaq100 14.713704778286651\n",
      "#gain 14.713704778286651\n",
      "#4 14.713704778286651\n",
      "10.49 14.713704778286651\n",
      "#exit 14.713704778286651\n"
     ]
    }
   ],
   "source": [
    "topic_words = top_words(W[:,4], word_list)\n",
    "for word, weight in topic_words[:10]:\n",
    "    print(word, weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version 24.535882454349068\n",
      "like 15.671817499585392\n",
      "buy 12.403599637748563\n",
      "actually 12.267941227174534\n",
      "alone 12.267941227174534\n",
      "#stupid 12.267941227174534\n",
      "@disney 12.267941227174534\n",
      "#lame 12.267941227174534\n",
      "#starwars 12.267941227174534\n",
      "@itunesmovies 12.267941227174534\n"
     ]
    }
   ],
   "source": [
    "topic_words = top_words(W[:,9], word_list)\n",
    "for word, weight in topic_words[:10]:\n",
    "    print(word, weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
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