Script 'Toolbox CSS'
1
Introduction
2
Digital trace data
2.1
String manipulation
2.1.1
Basic manipulations
2.1.2
Regular expressions
2.1.3
More advanced string manipulation
2.2
Web scraping
2.2.1
HTML 101
2.2.2
Selecting relevant content
2.2.3
Scraping HTML pages with
rvest
2.2.4
Automating scraping
2.2.5
Forms
2.2.6
Scraping hacks
2.3
Application Programming Interfaces (APIs)
2.3.1
Obtaining their data
2.3.2
rtweet
3
Agent-based modeling
3.1
Building an ABM: Schelling model
3.1.1
Recap: functions, if…else, loops, and matrices
3.1.2
Schelling model
3.1.3
Results
3.2
How to report ABMs – the ODD protocol
3.2.1
Baldassarri and Bearman (2007) ODD’ed
3.3
Further links
4
Text Mining
4.1
Pre-processing: put it into tidy text format
4.1.1
Cleaning
4.1.2
unnest_tokens()
4.1.3
In a nutshell
4.2
Sentiment Analysis
4.2.1
Assessing the results
4.2.2
Assessing the quality of the rating
4.3
TF-IDF
4.4
Latent Dirichlet Allocation (LDA)
4.4.1
Document-term matrix
4.4.2
Inferring the number of topics
4.4.3
Sense-making
4.4.4
Document-topic probabilities
5
References
6
Exercises
6.1
Regular Expressions
6.2
rvest
6.3
APIs
6.4
Text Mining
7
References
Published with bookdown
Toolbox CSS
Chapter 5
References