May 11, 2016 - https://us.sagepub.com/en-us/nam/text-mining/book244124 ... Part III: Text Analysis Methods from the Humanities and Social Sciences ... Sentiment Analysis ... that point to online data sources and other free online resources.
Text Mining A Guidebook for the Social Sciences Gabe Ignatow - University of North Texas Rada Mihalcea - University of Michigan
May 2016 | 208 pages | SAGE Publications, Inc
Format
Published Date
ISBN
Price
Paperback
05/11/2016
9781483369341
$50.00
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. Table Of Contents:
Part 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
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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 https://us.sagepub.com/en-us/nam/text-mining/book244124
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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
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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
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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
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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
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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
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Social and Computer Science Collaboration
Features/New To This Edition: KEY FEATURES: Unique coverage of theory, metatheory, research ethics, research design, and advanced technical tools prepares social science researchers to use text mining and text analysis in their own work. Guidance on research design, selecting and sampling data, and drawing inferences from data helps researchers maximize the impact of their work. Coverage of fundamental tools used in text mining methodologies includes web scraping and crawling, lexical resources, text processing, and supervised learning. Research from a wide range of disciplines, including anthropology, computer science, educational research, marketing, political science, psychology, and sociology, makes the book useful for researchers throughout the social sciences. Chapter-ending research exercises that point to online data sources and other free online resources help readers master concepts and techniques. Reviews: Text 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 The University of Texas at El Paso
This 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 London School of Economics
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