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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="generator" content="pandoc">
<title>Software Carpentry: 자연어 처리 - 텍스트</title>
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<body class="lesson">
<div class="container card">
<div class="banner">
<a href="http://software-carpentry.org" title="Software Carpentry">
<img alt="Software Carpentry banner" src="img/software-carpentry-banner.png" />
</a>
</div>
<article>
<div class="row">
<div class="col-md-10 col-md-offset-1">
<h1 class="title">자연어 처리 - 텍스트</h1>
<aside class="callout panel panel-info">
<div class="panel-heading">
<h3 id="ai-is-a-superpower"><span class="glyphicon glyphicon-pushpin"></span>AI is a Superpower</h3>
</div>
<div class="panel-body">
<p>“AI is a superpower!!!”, 인공지능을 체득하면 슈퍼파워를 손에 쥘 것이다. <a href="https://twitter.com/andrewyng/status/728986380638916609">Andrew Ng</a></p>
<p>금수저, 은수저 슈퍼파워를 받은 사람과 기계학습을 통달한 흑수저들간의 무한경쟁이 드뎌 시작되었다. 물론, 금수저를 입에 물고 기계학습을 통달한 사람이 가장 유리한 출발을 시작한 것도 사실이다.</p>
</div>
</aside>
<h2 id="학습목차">학습목차</h2>
<ol style="list-style-type: decimal">
<li><a href="nlp-text.html">텍스트 데이터</a>
<ol style="list-style-type: decimal">
<li><a href="nlp-bag-of-words.html">단어주머니(Bag of Words)</a></li>
<li><a href="https://statkclee.github.io/ds-authoring/">텍스트 데이터와 저작</a></li>
<li><a href="nlp-zipf-law.html">지프 법칙(Zipf law) - 시군 인구</a><br />
</li>
<li><a href="nlp-text-twitter.html">텍스트 데이터 – 트위터</a></li>
</ol></li>
<li><strong>텍스트 데이터 다루기</strong>
<ol style="list-style-type: decimal">
<li><a href="text-constitution.html">대한민국 헌법</a></li>
<li><a href="nlp-number-to-text.html">숫자를 문자로 표현</a></li>
<li><a href="nlp-stringr.html">stringr을 통해 문자열/텍스트 다루기</a></li>
</ol></li>
<li>단어주머니(Bag of Words)와 TF-IDF
<ol style="list-style-type: decimal">
<li><a href="nlp-bow-tf-idf.html">BoW와 TF-IDF</a></li>
</ol></li>
<li>탐색적 데이터 분석
<ol style="list-style-type: decimal">
<li><a href="nlp-amazon-google.html">아마존 vs. 구글</a></li>
<li><a href="nlp-movie-play.html">해외연극 등장인물 출현횟수</a></li>
<li><a href="nlp-movie-arch101.html">국내영화(건축학 개론) 등장인물 출현횟수</a></li>
<li><a href="nlp-text-basic.html">소설 텍스트 데이터 분석 – 소나기</a></li>
<li><a href="nlp-book.html">저녁이 있는 삶 – 손학규</a></li>
<li><a href="http://statkclee.github.io/politics/text-classify-speeches.html">연설문 판별 - 오바마 vs. 롬니</a></li>
</ol></li>
<li><strong>감성분석(Sentiment Analysis)</strong>
<ol style="list-style-type: decimal">
<li><a href="nlp-sentiment.html">감성분석(Sentiment Analysis) - 깔끔한 텍스트 방식(tidytext)</a></li>
<li><a href="nlp-text-sentiment-yelp.html">고객 방문후기 평점 감성분석 - 옐프(Yelp)</a></li>
<li><a href="nlp-english-textbook.html">영어 교과서 감성분석</a></li>
</ol></li>
<li><a href="nlp-topic-modeling.html">토픽 모형 (Topic Model)</a>
<ol style="list-style-type: decimal">
<li><a href="silge-topic-modeling.html">셜록홈즈 - 단어구름에서 토픽모형</a></li>
<li><a href="nlp-twitter-tidytext.html">트위터 - <code>tidyverse</code> + <code>tidytext</code></a></li>
</ol></li>
<li><strong>기계학습과 딥러닝(Deep Learning)</strong>
<ol style="list-style-type: decimal">
<li><a href="nlp-tm-tidytext.html">객체 변환: <code>tm</code> ↔ <code>tidytext</code></a></li>
<li><a href="nlp-text-classification.html"><code>tm</code>: 텍스트 분류(Text Classification) - 나이브 베이즈(naive bayes)</a></li>
<li><a href="nlp-text-classification-tidytext.html"><code>tidytext</code>: 텍스트 분류(Text Classification) - 나이브 베이즈(naive bayes)</a></li>
<li><a href="nlp-text-movielens.html">영화 평점 - 무비렌즈(MovieLens)</a></li>
<li><a href="nlp-spam-machine-learning.html">SMS 스팸분류 - Random Forest</a></li>
<li><a href="nlp-regex-word2vec.html">정규표현식에서 워드2벡(Word2Vec)</a></li>
<li><a href="text-kaggle-ecommerce-review.html">캐글 - 전자상거래 옷 리뷰</a></li>
<li><a href="text-twitter-tidytext-caret.html">재난 트윗 분류기 - <code>tidytext</code>와 <code>caret</code></a></li>
</ol></li>
<li><strong>한국어</strong>
<ol style="list-style-type: decimal">
<li><strong>한국어 R Meetup - 류충현</strong>: <a href="nlp-president-crawl.html">대통령 연설문 - 데이터 긁어오기</a>, <a href="nlp-president-dtm.html">대통령 연설문 - DTM 만들기</a></li>
<li><a href="nlp-rmecabko-install.html"><code>RmecabKo</code> 설치 - 맥(Mac)</a></li>
</ol></li>
<li><strong><a href="text-r-meet-python.html">R 파이썬을 만나다.</a></strong>
<ol style="list-style-type: decimal">
<li><a href="nlp-intro-python.html">자연어 처리 입문</a> - 텍스트 → 단어주머니(Bag of Words)</li>
<li><a href="nlp-intermediate-python.html">자연어 처리 중급</a></li>
<li><a href="text-python-datatype.html">데이터과학 - 파이썬 자료구조</a></li>
</ol></li>
<li><a href="langcon-2019-tutorial.html">LangCon - 자연어 처리의 화장을 하지 않은 얼굴: 튜토리얼</a>
<ul>
<li>영문
<ol style="list-style-type: decimal">
<li><a href="langcon-keynes-hayek.html">케인즈 vs 하이에크</a></li>
<li><a href="langcon-keynes-hayek-sentiment.html">케인즈 vs 하이에크 - 감성, 핵심어, 연관단어</a></li>
<li><a href="langcon-keynes-hayek-topic.html">케인즈 vs 하이에크 - 토픽모형</a></li>
<li><a href="langcon-keynes-hayek-spacyr.html">케인즈 vs 하이에크 - <code>spacyr</code></a></li>
</ol></li>
<li>유튜브
<ol style="list-style-type: decimal">
<li>한글
<ol style="list-style-type: decimal">
<li><a href="nlp-naver-news.html">네이버 뉴스</a></li>
<li><a href="nlp-tidytext-moon-speech.html">깔끔한 텍스트 (Tidytext) - 신년기자회견(2019)</a></li>
<li><a href="nlp-naver-news-mecab.html">네이버 뉴스 - RmecabKo (형태소 분석)</a><br />
</li>
</ol></li>
<li><a href="nlp-youtube-comment.html">유트브 댓글</a></li>
<li><a href="youtube-channel-comment-classification.html">알릴레요 vs. 홍카콜라 - 댓글 분류</a></li>
</ol></li>
</ul></li>
</ol>
</div>
</div>
</article>
<div class="footer">
<a class="label swc-blue-bg" href="http://software-carpentry.org">Software Carpentry</a>
<a class="label swc-blue-bg" href="https://github.com/swcarpentry/lesson-template">Source</a>
<a class="label swc-blue-bg" href="mailto:[email protected]">Contact</a>
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