Text Analytics for Corpus Linguistics and Digital Humanities: Simple R Scripts and Tools
About
Do you want to gain a deeper understanding of how big tech analyzes and exploits our text data, or investigate how political parties differ by analyzing textual styles in documents?
This book explores how to apply state-of-the-art text analytics methods to detect and visualize phenomena in text data. Solidly based on methods from corpus linguistics, natural language processing, text analytics and digital humanities, this book shows readers how to conduct experiments with their own corpora and research questions, underpin their theories, quantify the differences and pinpoint characteristics. Case studies and experiments are detailed in every chapter using real-world and open access corpora from politics, World English, history, and literature. The results are interpreted and put into perspective, pitfalls are pointed out, and necessary pre-processing steps are demonstrated. This book also demonstrates how to use the programming language R, as well as simple alternatives and additions to R, to conduct experiments and employ visualisations by example, with extensible R-code, recipes, links to corpora, and a wide range of methods. The methods introduced can be used across texts of all disciplines, from history or literature to party manifestos and patient reports.
Chapter 3
Chapter 4
Grimm Fairy Tales text
Debates Keywords in r
Chapter 5
SEL_perparty_v2.csv with LightSide
R Script: 05_doc_class_script.R
Chapter 6
CLMET corpus as it is distributed (p. 116)
/Data/CLMET_distribution.zip (original, as distributed)
/Data/CLMET_distribution/corpus/txt/plain/ (as .zip file)
PLAIN_ALL.mallet
/Data/CLMET/corpus/txt/plain_per_period/
CLMET3_1_1to3_forMallet100.Periodtextmarked3_ln.csv
CLMET3_1_1to3_forMallet100.Periodtextmarked3.mallet
CLMET3_1_1to3_forMallet100.Periodtextmarked3.keys.15.txt
/Data/presidential-speeches-master/TrumpVSObama2.csv
R Script (p. 123 ff): 06_TopicModel_stm.R
Chapter 7
--> Windows Installations, Silvio Liesch
/Data/CLMET/corpus/txt/plain_per_period/
CLMET3_1_1to3_forMallet100.Periodtextmarked2.csv
CLMET3_1_1to3_forMallet100.Periodtextmarked2.t500.skim7.gml
CLMET3_1_1to3_forMallet100.Periodtextmarked2.t500.skim7.pdf
/Data/CORPS_II_RELEASE202/
SEL_perparty_v2.t500.skim5.bw2000.gml
SEL_perparty_v2.t500.skim5.bw2000.pdf
NO R script
Chapter 8
Figure 8.1 ICE-GB NOT provided
Data/CORPS_II_RELEASE202/
SEL_perparty_v2_reponly.csv
SEL_perparty_v2_demonly.csv
SEL_perparty_v2_reponly_out.txt
SEL_perparty_v2_demonly_out.txt
SEL_perparty_v2_demonly_out.txt.bin
SEL_perparty_v2_reponly_out.txt.bin
SEL_perparty_v2_demonly2_out.txt.bin
SEL_perparty_v2_reponly2_out.txt.bin
Update link to
https://sites.google.com/site/fritzgntr/software-resources/semantic_spaces (p.164)
R Script: 08_DistrSem.R
Chapter 9
09_BERT_usingmylocalConda.R
09_BERT_experiment.R
Box9.6_Figure9.1
BERT_parties_presidential_minus1_Rplot
BERT_parties_presidential_Rplot
