text: <style>
  
  .reveal section p {
    color: black;
    font-size: .7em;
    font-weight: normal;
    font-family: 'Helvetica'; #this is the font/color of text in slides
  }
  
  
  .section .reveal .state-background {
      background: white;}
  .section .reveal h1,
  .section .reveal p {
      color: black;
      position: relative;
      font-family: 'Helvetica';
      font-weight: normal;
      top: 4%;}
     
   
   /* section titles */
  .reveal h1 { 
    color: black;
    position: relative;
    font-weight: normal;
    font-family: 'Helvetica'; 
    top: 4%
  }    
  
   
  /* slide titles */
  .reveal h3 { 
    color: black;
    font-weight: normal;
    font-family: 'Helvetica'; 
  }    
  
  .small-code pre code {
    font-size: 1.2em;
  }
  
  </style>
  
  
  Intro to Text Analysis
  ========================================================
  author: Chris Bail 
  date: Duke University
  autosize: true
  transition: fade  
    website: https://www.chrisbail.net  
    Twitter: https://www.twitter.com/chris_bail  
    github: https://github.com/cbail  
  
  
  WWI
  ========================================================
  
  <img src="ww1.jpg" height="500" />
  
  
  Harold Laswell
  ========================================================
  
  <img src="laswell.jpg" height="500" />
  
  
  Harold Laswell (1938)
  ========================================================
  &nbsp; 
  
  "We may classify references into categories according to the understanding which prevails among those who are accustomed to the symbols. References used in interviews may be quantified by counting the number of references which fall into each category during a selected period of time (or per thousand words uttered)."
  
  
  Ahead of his time?
  ========================================================
  
  <img src="pulse_machine.jpg" height="500" />
  
  
  
  
  
  Timeline of Quantitative Text Analysis
  ========================================================
  &nbsp;
  
  Time | Activity
  ------------- | -------------
  1934 | Laswell Produces first Key-Word Count
  1934 | Vygotsky Produces first Quantitative Narrative Analaysis
  1950 | Gottschalk Uses Content Analysis to Track Freudian Themes
  1950 | Turing Applies AI to text
  1952 | Bereleson Publishes First Textbook on Content Analysis
  1954 | First Automatic Translation of Text (Georgetown Experiment)
  1963 | Msteller and Wallace analyze Federalist Papers
  1965 | Tomashevsky Further Formalizes Quantitative Narrative Analysis
  
  Timeline of Quantitative Text Analysis
  ========================================================
  &nbsp;
  
  Time | Activity
  ------------- | -------------
  1966 | Stone and Bales use mainframe computer to measure psychometric properties of text at RAND
  1980 | Decline of Chomskyean Formalism/NLP is Born
  1980 | Machine Learning is Applied to NLP
  1981 | Weintraub counts parts of speech
  1985 | Schrodt Introduces Automated Event Coding
  1986 | Pennebaker develops LIWC
  1988 | First Latent Semantic Analysis Patent
  1989 | Franzosi brings Quantitative Narrative Analysis to Social Science
  
  
  Timeline of Quantitative Text Analysis
  ========================================================
  &nbsp;
  
  Time | Activity
  ------------- | -------------
  1998 | Mohr's Quantitative Analysis of Culture
  1999 | Bearman et al. apply Network Methods to Narratives
  2001 | Blei et al. develop LDA
  2002 | MALLET created
  2005 | Quinn et al. use analyze political speeches using topic models
  2009 | New Directions in Text Conference
  2010 | King/Hopkins Bring Supervised Learning into mainstream
  2010 | Tools for Text Workshop at Washington
  
  An Interdisciplinary Field!
  ========================================================
  &nbsp;
  
  Opening Black Boxes
  ========================================================
  &nbsp;
  
  From Words to Numbers
  ========================================================
  &nbsp;
  
  
  
  Other Great Introductions
  ========================================================
  &nbsp;
  
  Justin Grimmer & Brandon Stewart. Text as Data: The Promises and Pitfalls of Automated Content Analysis, Political Analysis.
  
  James Evans & Pedro Aceves. Machine Translation: Mining Text for Social Theory. Annual Review of Sociology.
  
  
  
  