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Text Mining

Text Mining
A Guidebook for the Social Sciences

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June 2016 | 208 pages | SAGE Publications, Inc
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.

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Part I: Digital Texts, Digital Social Science
1. Social Science and the Digital Text Revolution
Learning Objectives  
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  
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  
Web Statistics  
Web Crawling  
Web Scraping  
Software for Web Crawling and Scraping  
4. Lexical Resources
Learning Objectives  
Roget's Thesaurus  
Linguistic Inquiry and Word Count  
General Inquirer  
Downloadable Lexical Resources and APIs  
5. Basic Text Processing
Learning Objectives  
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  
Conceptual Foundations  
Mixed Methods of Narrative Analysis  
Automated Approaches to Narrative Analysis  
Future Directions  
Specialized Software for Narrative Analysis  
9. Metaphor Analysis
Learning Objectives  
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  
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  
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  
Entity Extraction  
Relation Extraction  
Web Information Extraction  
Template Filling  
Software and Datasets for Information Extraction and Text Mining  
13. Information Retrieval
Learning Objectives  
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  
Theoretical Foundations  
Future Directions  
Software and Datasets for Word and Text Relatedness  
15. Topic Models
Learning Objectives  
Digital Humanities  
Political Science  
Software for Topic Modeling  
V: Conclusions
16. Text Mining, Text Analysis, and the Future of Social Science
Social and Computer Science Collaboration  


Student Resource Site

Visit the companion website for free access to data files and links to web resources.

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

Clear presentation of text mining best practices. It also calls attention to the need to develop complex interpretation strategies for data acquired through various mining practices.

Mr Elias Ortega-Aponte
Graduate Division of Religion, Drew University
September 9, 2016

Never received the review copy.

Dr Babette Protz
Humanities Division, Univ Of S Carolina-Lancaster
December 16, 2015

Sample Materials & Chapters

Chapter 3

Chapter 9

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ISBN: 9781483369341