Text Analytics for Corpus Linguistics and Digital Humanities
Simple R Scripts and Tools
Publisher: Bloomsbury Academic
Date of Publication: 2 May 2024
Number of Volumes: Hardback
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Product details:
ISBN13: | 9781350370821 |
ISBN10: | 1350370827 |
Binding: | Hardback |
No. of pages: | 240 pages |
Size: | 234x156 mm |
Language: | English |
Illustrations: | 25 bw illus |
700 |
Category:
Long description:
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, associations and trends in documents? Or create a map of a text collection and write a simple QA system yourself?
This open access 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.
The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.
This open access 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.
The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.
Table of Contents:
List of Figures
List of Tables
Acknowledgements
1. Introduction
2. Spikes of Frequencies and First Steps in UNIX
3. Frequency Lists and First Steps in R
4. Overuse and Keywords and Using R Libraries
5. Document Classification and Supervised ML in LightSide and R
6. Topic Modelling and Unsupervised ML with Mallet and R
7. Kernel Density Estimation for Conceptual Maps
8. Distributional Semantics
9. BERT Models
10. Conclusions
References
Index
List of Tables
Acknowledgements
1. Introduction
2. Spikes of Frequencies and First Steps in UNIX
3. Frequency Lists and First Steps in R
4. Overuse and Keywords and Using R Libraries
5. Document Classification and Supervised ML in LightSide and R
6. Topic Modelling and Unsupervised ML with Mallet and R
7. Kernel Density Estimation for Conceptual Maps
8. Distributional Semantics
9. BERT Models
10. Conclusions
References
Index