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cm013.html
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<h1 class="title toc-ignore">Text analysis: fundamentals and sentiment analysis</h1>
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<div id="cm013---november-7-2016" class="section level1">
<h1>cm013 - November 7, 2016</h1>
<div id="overview" class="section level2">
<h2>Overview</h2>
<ul>
<li>Identify the basic workflow for conducting text analysis</li>
<li>Define <em>sentiment analysis</em> and review how sentiment analysis was used in <em>Conservatives report, but liberals display, greater happiness</em></li>
<li>Define the <em>tidy text format</em></li>
<li>Demonstrate how to conduct sentiment analysis using David Robinson’s <a href="http://varianceexplained.org/r/trump-tweets/">Text analysis of Trump’s tweets confirms he writes only the (angrier) Android half</a></li>
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<li><a href="text01.html">Notes from class</a></li>
<li><a href="extras/cm013_slides.html">Slides</a></li>
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<h2>To do for Wednesday</h2>
<ul>
<li><a href="hw06-webdata.html">Finish homework 6</a></li>
<li>Review chapters 1-4 in <a href="http://tidytextmining.com/"><em>Tidy Text Mining with R</em></a> if you have not already</li>
<li>Chapters 5-7 in <a href="http://tidytextmining.com/"><em>Tidy Text Mining with R</em></a></li>
<li><a href="http://cacm.acm.org/magazines/2012/4/147361-probabilistic-topic-models/fulltext">Blei, D. M. (2012). Probabilistic topic models. <em>Communications of the ACM</em>, 55(4), 77-84.</a></li>
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