Text mining : a guidebook for the social sciences / Gabe Ignatow, University of North Texas ; Rada Mihalcea, University of Michigan.
2017
300.721 I24t 2017
Available at 3rd Floor
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Title
Text mining : a guidebook for the social sciences / Gabe Ignatow, University of North Texas ; Rada Mihalcea, University of Michigan.
Author
ISBN
9781483369341 (pbk. ; alk. paper)
148336934X (pbk. ; alk. paper)
148336934X (pbk. ; alk. paper)
Published
Los Angeles : SAGE, [2017]
Language
English
Description
xvi, 188 pages : illustrations ; 23 cm
Other Standard Identifiers
12696298
Call Number
300.721 I24t 2017
System Control No.
(OCoLC)933765455
(OCoLC)933765455
(OCoLC)933765455
Summary
"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.
Bibliography, etc. Note
Includes bibliographical references (pages 168-182) and index.
Formatted Contents Note
Social science and the digital text revolution
Research design strategies
Web crawling and scraping
Lexical resources
Basic text processing
Supervised learning
Thematic analysis, qualitative data analysis software, and visualization
Narrative analysis
Metaphor analysis
Word and text relatedness
Text classification
Information extraction
Information retrieval
Sentiment analysis
Topic models
Text mining, text analysis, and the future of social science.
Research design strategies
Web crawling and scraping
Lexical resources
Basic text processing
Supervised learning
Thematic analysis, qualitative data analysis software, and visualization
Narrative analysis
Metaphor analysis
Word and text relatedness
Text classification
Information extraction
Information retrieval
Sentiment analysis
Topic models
Text mining, text analysis, and the future of social science.
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