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Text Analytics for Corpus Linguistics and Digital Humanities: Simple R Scripts and Tools: Language, Data Science and Digital Humanities

Autor Gerold Schneider
en Limba Engleză Hardback – 29 mai 2024
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.
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Specificații

ISBN-13: 9781350370821
ISBN-10: 1350370827
Pagini: 240
Ilustrații: 25 bw illus
Dimensiuni: 156 x 234 mm
Greutate: 0.51 kg
Editura: Bloomsbury Publishing
Colecția Bloomsbury Academic
Seria Language, Data Science and Digital Humanities

Locul publicării:London, United Kingdom

Caracteristici

Shows readers how to use R to execute the realistic examples and apply the methods to data of their own

Notă biografică

Gerold Schneider is Adjunct Professor at the Department of Computational Linguistics of the University of Zurich, Switzerland.

Cuprins

List of FiguresList of TablesAcknowledgements1. Introduction2. Spikes of Frequencies and First Steps in UNIX3. Frequency Lists and First Steps in R4. Overuse and Keywords and Using R Libraries5. Document Classification and Supervised ML in LightSide and R6. Topic Modelling and Unsupervised ML with Mallet and R7. Kernel Density Estimation for Conceptual Maps8. Distributional Semantics9. BERT Models10. ConclusionsReferencesIndex