Text Mining: A Guidebook for the Social Sciences

Synopsis

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. 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. Gabe Ignatow, University Of North Texas ; Rada Mihalcea, University Of Michigan. Includes Bibliographical References (pages 168-182) And Index.