Text Analytics for Corpus Linguistics and Digital Humanities : Simple R Scripts and Tools / Gerold Schneider.

Format:

Book

Author/Creator:

Schneider, Gerold, author.

Series:

Language, Data Science and Digital Humanities.

Language, Data Science and Digital Humanities

Language:

English

Subjects (All):

Corpora (Linguistics)--Data processing.

Corpora (Linguistics).

Digital humanities--Research Methodology.

Digital humanities.

R (Computer program language).

Physical Description:

1 online resource (241 pages)

Edition:

1st ed.

Distribution:

London : Bloomsbury Publishing (UK), 2024.

Place of Publication:

London : Bloomsbury Academic, 2024.

System Details:

text file HTML

Summary:

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 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.

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

Notes:

Includes bibliographical references and index.

ISBN:

1-350-37085-1

1-350-37084-3

1-350-37083-5

OCLC:

1425791730