Text mining : a guidebook for the social sciences /
Ignatow, Gabriel,
Text mining : a guidebook for the social sciences / Gabe Ignatow, University of North Texas, Rada Mihalcea, University of Michigan. - xvi, 188 pages : illustrations ; 23 cm
Includes bibliographical references and index.
Part I: Digital Texts, Digital Social Science -- 1. Social Science and the Digital Text Revolution -- 2. Research Design Strategies -- Part II: Text Mining Fundamentals -- 3. Web Crawling and Scraping -- 4. Lexical Resources -- 5. Basic Text Processing -- 6. Supervised Learning -- Part III: Text Analysis Methods from the Humanities and Social Sciences -- 7. Thematic Analysis, QDAS, and Visualization -- 8. Narrative Analysis -- 9. Metaphor Analysis -- Part IV: Text Mining Methods from Computer Science -- 10. Word and Text Relatedness -- 11. Text Classification -- 12. Information Extraction -- 13. Information Retrieval -- 14. Sentiment Analysis -- 15. Topic Models -- Part V: Conclusions -- 16. Text Mining, Text Analysis, and the Future of Social Science.
"Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively." --Publisher's website.
148336934X 9781483369341
2015044977
Social sciences--Research--Methodology.
Discourse analysis--Data processing
Natural language processing (Computer science)
Data mining.
H61.3 / .I395 2017
300.721
Text mining : a guidebook for the social sciences / Gabe Ignatow, University of North Texas, Rada Mihalcea, University of Michigan. - xvi, 188 pages : illustrations ; 23 cm
Includes bibliographical references and index.
Part I: Digital Texts, Digital Social Science -- 1. Social Science and the Digital Text Revolution -- 2. Research Design Strategies -- Part II: Text Mining Fundamentals -- 3. Web Crawling and Scraping -- 4. Lexical Resources -- 5. Basic Text Processing -- 6. Supervised Learning -- Part III: Text Analysis Methods from the Humanities and Social Sciences -- 7. Thematic Analysis, QDAS, and Visualization -- 8. Narrative Analysis -- 9. Metaphor Analysis -- Part IV: Text Mining Methods from Computer Science -- 10. Word and Text Relatedness -- 11. Text Classification -- 12. Information Extraction -- 13. Information Retrieval -- 14. Sentiment Analysis -- 15. Topic Models -- Part V: Conclusions -- 16. Text Mining, Text Analysis, and the Future of Social Science.
"Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively." --Publisher's website.
148336934X 9781483369341
2015044977
Social sciences--Research--Methodology.
Discourse analysis--Data processing
Natural language processing (Computer science)
Data mining.
H61.3 / .I395 2017
300.721