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Text mining: a guidebook for the social sciences

By: Ignatow, Gabe.
Contributor(s): Mihalcea, Rada.
Material type: materialTypeLabelBookPublisher: Thousand Oaks Sage 2017Description: xvi, 188 p.ISBN: 9781483369341.Subject(s): Research Methodology - Social Sciences | Discourse analysis - Data Processing | Communication - Network Analsysis | Natural Language Processing - Computer Science | Data MiningDDC classification: 300.721 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. https://in.sagepub.com/en-in/sas/text-mining/book244124
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Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
Slot 232 (0 Floor, West Wing) Non-fiction 300.721 I4T3 (Browse shelf) Checked out 05/08/2019 193664

Table of Contents:

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.

https://in.sagepub.com/en-in/sas/text-mining/book244124

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