{"product_id":"text-mining-9781483369341","title":"Text Mining","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOnline 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. \u003cstrong\u003eText Mining\u003c\/strong\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eText Mining and Analysis is a comprehensive book that deals with the latest developments of text mining research, methodology, and applications. An excellent choice for anyone who wants to learn how these emerging practices can benefit their own research in an era of Big Data. -- Kenneth C. C. Yang\u003cbr\u003eThis is a clear, comprehensive and thorough description of new text mining techniques and their applications: a \"must\" for students and social researchers who wish to understand how to tackle the challenges raised by Big Data. -- Aude Bicquelet\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I: Digital Texts, Digital Social Science 1. Social Science and the Digital Text Revolution    Learning Objectives    Introduction    History of Text Analysis    Risk and Rewards of Text Mining for the Social Sciences    Social Data from Digital Environments    Theory and Metatheory    Ethics of Text Mining    Organization of This Volume 2. Research Design Strategies    Learning Objectives    Introduction    Levels of Analysis    Strategies for Document Selection and Sampling    Types of Inferential Logic    Approaches to Research Design    Part II: Text Mining Fundamentals 3. Web Crawling and Scraping    Learning Objectives    Introduction    Web Statistics    Web Crawling    Web Scraping    Software for Web Crawling and Scraping 4. Lexical Resources    Learning Objectives    Introduction    WordNet    Roget′s Thesaurus    Linguistic Inquiry and Word Count    General Inquirer    Wikipedia    Downloadable Lexical Resources and APIs 5. Basic Text Processing    Learning Objectives    Introduction    Tokenization    Stopword Removal    Stemming and Lemmatization    Text Statistics    Language Models    Other Text Processing    Software for Text Processing 6. Supervised Learning    Learning Objectives    Feature Representation and Weighting    Supervised Learning Algorithms    Evaluation of Supervised Learning    Software for Supervised Learning Part III: Text Analysis Methods from the Humanities and Social Sciences 7. Thematic Analysis, QDAS, and Visualization    Learning Objectives    Thematic Analysis    Qualitative Data Analysis Software    Visualization Tools 8. Narrative Analysis    Learning Objectives    Introduction    Conceptual Foundations    Mixed Methods of Narrative Analysis    Automated Approaches to Narrative Analysis    Future Directions    Specialized Software for Narrative Analysis 9. Metaphor Analysis    Learning Objectives    Introduction    Theoretical Foundations    Qualitative Metaphor Analysis    Mixed Methods of Metaphor Analysis    Automated Metaphor Identification Methods    Software for Metaphor Analysis Part IV: Text Mining Methods from Computer Science 10. Word and Text Relatedness    Learning Objectives    Introduction    Theoretical Foundations    Corpus-based and Knowledge-based Measures of Relatedness    Software and Datasets for Word and Text Relatedness    Further Reading 11. Text Classification    Learning Objectives    Introduction    Applications of Text Classification    Representing Texts for Supervised Text Classification    Text Classification Algorithms    Bootstrapping in Text Classifcation    Evaluation of Text Classification    Software and Datasets for Text Classification 12. Information Extraction    Learning Objectives    Introduction    Entity Extraction    Relation Extraction    Web Information Extraction    Template Filling    Software and Datasets for Information Extraction and Text Mining 13. Information Retrieval    Learning Objectives    Introduction    Theoretical Foundations    Components of an Information Retrieval System    Information Retrieval Models    The Vector-Space Model    Evaluation of Information Retrieval Models    Web-Based Information Retrieval    Software and Datasets for Information Retrieval 14. Sentiment Analysis    Learning Objectives    Introduction    Theoretical Foundations    Lexicons    Corpora    Tools    Future Directions    Software and Datasets for Word and Text Relatedness 15. Topic Models    Learning Objectives    Introduction    Digital Humanities    Political Science    Sociology    Software for Topic Modeling V: Conclusions 16. Text Mining, Text Analysis, and the Future of Social Science    Introduction    Social and Computer Science Collaboration","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":51019934859607,"sku":"9781483369341","price":72.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781483369341.jpg?v=1750781792","url":"https:\/\/bookcurl.com\/products\/text-mining-9781483369341","provider":"Book Curl","version":"1.0","type":"link"}