Text Mining with R Book Club
Welcome
Book club meetings
Pace
Book Chapters
1
The Tidy Text Format
1.1
Contrasting Tidy Text with Other Data Structures
1.2
The unnest_tokens Function
1.3
Example 1: Tidying the works of Jane Austen
1.4
Example 2: The
gutenbergr
package
1.5
A flowchart of a typical text analysis using tidy data priciples.
1.6
Meeting Videos
1.6.1
Cohort 1
2
Sentiment analysis with tidy data
2.1
Sentiment analysis with tidy data
2.2
Sentiment/emotion Lexicons
2.3
Sentiment analysis with inner join
2.4
Examining how sentiment changes in each novel
2.5
Comparing the three sentiment dictionaries
2.6
Most common positive and negative words
2.7
Wordclouds
2.8
Looking at units beyond just words
2.9
Meeting Videos
2.9.1
Cohort 1
3
Analyzing word and document frequency: tf-idf
3.1
Meeting Videos
3.1.1
Cohort 1
4
Relationships between words: n-grams & correlations
4.1
Objectives:
4.2
Tokeninzing by n-grams
4.2.1
Analyzing bigrams
4.2.2
Using bigrams to provide context in sentiment analysis
4.2.3
Visualizing network of bigrams with ggraph
4.3
Counting and correlating pairs of words with
widyr
4.3.1
Pairwise correlation
4.4
Meeting Videos
4.4.1
Cohort 1
5
Converting to and from non-tidy formats
5.1
Meeting Videos
5.1.1
Cohort 1
6
Topic modeling
6.1
Meeting Videos
6.1.1
Cohort 1
7
Case study: comparing Twitter archives
7.1
Meeting Videos
7.1.1
Cohort 1
8
Case study: mining NASA metadata
8.1
Meeting Videos
8.1.1
Cohort 1
9
Case Study: Analyzing usenet Text
9.1
Objectives
9.2
9.1 Pre-processing
9.2.1
Pre-processing text
9.3
9.2 Words in newsgroups
9.4
9.3 Sentiment Analysis
9.4.1
Question 2:
9.5
Meeting Videos
9.5.1
Cohort 1
Published with bookdown
Text Mining with R Book Club
9.1
Objectives
Understand the
usenet
data via pre-processing
Understand how to apply tf-idf & topic modeling to the usenet data
Understand how to apply sentiment analysis and n-gram analysis to usenet data