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Text Analysis in R
========================================================
author: Wouter van Atteveldt
date: Session 5: Sentiment Analysis and Machine Learning

Course Overview
===
type:section 

Thursday: Introduction to R

Friday: Corpus Analysis & Topic Modeling

Saturday:
 + *Sentiment Analysis*
 + Machine Learning

Sunday:
 + Basic visualization
 + Semantic Network Analysis
 + Graph Visualization


Sentiment Analysis
====

+ What is the tone of a text?
+ Techniques (e.g. Pang/Lee 2008, Liu 2012)
  + Manual coding
  + Dictionaries
  + Machine Learning
  + Crowdsourcing (Benoit ea 2015)
  
Sentiment Analysis: problems
====

<em>"The man who leaked cell-phone coverage of Saddam Hussein's execution was arrested"</em>
  
+ Language is subjective, ambiguous, creative
+ What does positive/negative mean?
  + e.g. Osgood ea 1957: evaluation, potency, activity
+ Who is positive/negative about what?
  + Sentiment Attribution

Sentiment Analysis resources
====

+ Lexicon (dictionary)
+ Annotated texts
+ Tools / models

Interlude: Downloading and Parsing files
===
type: section

Lexical Sentiment Analysis
===

+ Get list of positive / negative terms
+ Count occurrences in text
+ Summarize to sentiment score
+ Possible improvements
  + Word-window approach (tomorrow)
  + Deal with negation, intensification

Lexical Sentiment Analysis in R
===

+ Nothing new here!
+ Directly count words in DTM:

```{r, eval=F}
library(slam)
reviews$npos = row_sums(dtm[, colnames(dtm) %in% pos_words])
```

+ Count words in token list:

```{r, eval=F}
tokens$sent[tokens$lemma %in% pos_words] = 1
```

Interactive Session 5a
====
type: section

Lexical Sentiment Analysis


Course Overview
===
type:section 

Thursday: Introduction to R

Friday: Corpus Analysis & Topic Modeling

Saturday:
 + Sentiment Analysis
 + *Machine Learning*

Sunday:
 + Basic visualization
 + Semantic Network Analysis
 + Graph Visualization
 
 
Machine Learning
====

+ Statistical Modeling
  + Dependent Variable: sentiment, topic, frame
  + Independent Variables: words
+ Focus: prediction rather than explanation
  + Millions of correlated independent variables
  
Text Classification
===

+ Each text has a 'class'
+ Training documents to fit model
+ Test documents to gauge accuracy
  + (or use cross-validation)
+ Choices:
  + What features?
  + Which model?
  
Text Classification: features
===

+ Features: independent variables
+ Basic approach: each word is a feature
+ Other options e.g.
  + Collocations (n-grams)
  + LDA Topics
  + Feature selection

Text Classification: models
===

+ Naive Bayes
+ Maximum Entropy
+ Support Vector Machines
+ Neural Networks
+ (deep learning)

Combining models
===

+ Ensemble Learning
  + Train multiple models 
  + Decide by vote
+ Active Learning
  + Code limited amount of material
  + Train+test model
  + Code most difficult cases, repeat

Text Classification in R
===

+ Package RTextTools
  + Jurka et al, 2013
+ Based on DTM plus coded classes
+ Does learning, evaluation, prediction

Text Classification in R
===

(1) Create 'container' from DTM + coded classes

```{r, eval=F}
library(RTextToools)
c = create_container(dtm, classes, 
  trainSize=train, testSize=test, virgin=F)
```
(2) Train and test model

```{r, eval=F}
SVM <- train_model(c,"SVM")
SVM_CLASSIFY <- classify_model(c, SVM)
```

(3) Evaluate

```{r, eval=F}
analytics <- create_analytics(c, SVM_CLASSIFY)
```

Code new material
===

```{r, eval=F}
is_coded = !is.na(classes)
c = create_container(dtm, classes, 
  trainSize=is_coded, virgin=T)
SVM <- train_model(c,"SVM")
SVM_CLASSIFY <- classify_model(c, SVM)
analytics <- create_analytics(c, SVM_CLASSIFY)
head(analytics@document_summary)
```

Some links:
===

+ Burscher et al 2014: Framing with SVM's
+ Purpura & Wilkerson 2007: Active Learning for Agenda Coding

+ Some online resources:
  + http://www.r-bloggers.com/sentiment-analysis-on-donald-trump-using-r-and-tableau/
  + https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon
  + http://datascienceplus.com/sentiment-analysis-with-machine-learning-in-r/
  + https://sites.google.com/site/miningtwitter/questions/sentiment/sentiment


Interactive Session 5b
====
type: section

Text Classification for Sentiment Analysis

Hands-on session 5
====
type: section

Break

Handouts:
+ Sentiment Analysis Resources
+ Lexcial Sentiment Analysis
+ Machine Learning