Text mining : a guidebook for the social sciences / Gabe Ignatow, University of North Texas, Rada Mihalcea, University of Michigan.

Format:

Book

Author/Creator:

Ignatow, Gabe, author.

Radev, Dragomir, 1968- author.

Contributor:

Esther F. Kantrowitz & Lionel Kantrowitz Collection Endowment Fund.

Language:

English

Subjects (All):

Social sciences--Research--Methodology.

Social sciences.

Discourse analysis--Data processing.

Discourse analysis.

Communication--Network analsysis.

Communication.

Natural language processing (Computer science).

Data mining.

Physical Description:

xvi, 188 pages : illustrations ; 23 cm

Place of Publication:

Los Angeles : SAGE, [2017]

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, this book helps readers use text mining techniques more efficiently and productively. Book jacket.

Contents:

Part I Digital Texts, Digital Social Science 1

1 Social Science and the Digital Text Revolution 2

History of Text Analysis 3

Risks and Rewards of Text Mining for the Social Sciences 5

Social Data From Digital Environments 6

Theory and Metatheory 10

Ethics of Text Mining 12

Participant Consent, Privacy, and Anonymity 12

Prompted and Unprompted Data 13

Organization of This Volume 13

2 Research Design Strategies 16

Levels of Analysis 18

The Textual Level 18

The Contextual Level 18

The Sociological Level 18

Strategies for Document Selection and Sampling 19

Case Selection 19

Text Sampling 20

Types of Inferential Logic 22

Inductive Logic 23

Deductive Logic 24

Abductive Logic 25

Approaches to Research Design 27

Analysis of Discourse Positions 27

Conversation Analysis 28

Critical Discourse Analysis 28

Content Analysis 29

Foucauldian Intertextuality 30

Analysis of Texts as Social Information 31

Part II Text Mining Fundamentals 33

3 Web Crawling and Scraping 34

Web Statistics 36

Web Crawling 37

Process Steps in Crawling 37

Traversal Strategies 38

Crawler Politeness 38

Web Scraping 39

Software for Web Crawling and Scraping 41

4 Lexical Resources 42

WordNet 43

WordNet-Affect 45

Roget's Thesaurus 46

Linguistic Inquiry and Word Count 46

General Inquirer 48

Wikipedia 48

Wiktionary 51

Downloadable Lexical Resources and Application Program Interfaces 51

5 Basic Text Processing 52

Tokenization 54

Stop Word Removal 55

Stemming and Lemmatjzation 55

Text Statistics 56

Language Models 59

Other Text Processing 60

Part of Speech Tagging 60

Collocation identification 60

Syntactic Parsing 61

Named Entity Tagging 61

Word Sense Disambiguation 61

Software for Text Processing 61

6 Supervised Learning 62

Feature Representation and Weighting 65

Feature Weighting 65

Supervised Learning Algorithms 66

Decision Trees 67

Instance-Based Learning 68

Support Vector Machines 69

Evaluation of Supervised Learning 71

Software for Supervised Learning 71

Part III Text Analysis Methods from the Humanities and Social Sciences 73

7 Thematic Analysis, Qualitative Data Analysis Software, and Visualization 74

Thematic Analysis 75

Qualitative Data Analysis Software 77

Visualization Tools 83

Word Clouds 84

Word Trees and Phrase Nets 84

Matrices and Maps 85

Key Word in Context 86

Software for Thematic Analysis, Qualitative Data Analysis and Visualization 86

8 Narrative Analysis 88

Conceptual Foundations 90

Structural Approaches to Narrative 90

Functionalist Approaches to Narrative 91

Sociological Approaches to Narrative 92

Mixed Methods of Narrative Analysis 92

Automated Methods of Narrative Analysis 93

Future Directions 93

Software for Narrative Analysis 94

9 Metaphor Analysis 96

Theoretical Foundations 98

Qualitative Metaphor Analysis 99

Anthropology 99

Educational Research 99

Political Science 100

Psychology 100

Sociology 101

Mixed Methods of Metaphor Analysis 101

Management Research 101

Psychology 102

Sociology 102

Automated Metaphor Identification Methods 103

Software for Metaphor Analysis 103

Part IV Text Mining Methods from Computer Science 105

10 Word and Text Relatedness 106

Theoretical Foundations 107

Corpus-Based and Knowledge-Based Measures of Relatedness 108

Corpus-Based Measures of Word Relatedness 108

Knowledge-Based Measures of Word Relatedness 110

Measures of Text Relatedness 112

Software and Data Sets for Word and Text Relatedness 114

11 Text Classification 116

A Brief History of Text Classification 118

Applications of Text Classification 119

Topic Classification 119

E-Mail Spam Detection 120

Sentiment Analysis/Opinion Mining 120

Gender Classification 120

Deception Detection 122

Other Applications 122

Representing Texts for Supervised Text Classification 122

Feature Weighting and Selection 123

Text Classification Algorithms 124

Naive Bayes 124

Rocchio Classifier 125

Bootstrapping in Text Classification 126

Evaluation of Text Classification 127

Software and Data Sets for Text Classification 127

12 Information Extraction 130

Entity Extraction 132

Relation Extraction 133

Web Information Extraction 134

Template Filling 135

Software and Data Sets for Information Extraction and Text Mining 135

13 Information Retrieval 136

Theoretical Foundations 138

Components of an Information Retrieval System 138

Information Retrieval Models 140

The Vector Space Model 142

Evaluation of Information Retrieval Models 144

Web-Based Information Retrieval 145

Software and Data Sets for Information Retrieval 147

14 Sentiment Analysis 148

Theoretical Foundations 150

Lexicons 151

Corpora 152

Tools 153

Software and Data Sets for Sentiment Analysis 154

15 Topic Models 156

Digital Humanities 160

Political Science 160

Sociology 161

Software far Topic Modeling 161

Part V Conclusions 163

16 Text Mining, Text Analysis, and the Future of Social Science 164

Social and Computer Science Collaboration 166.

Notes:

Includes bibliographical references (pages 168-182) and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Esther F. Kantrowitz & Lionel Kantrowitz Collection Endowment Fund.

ISBN:

9781483369341

148336934X

OCLC:

933765455

Publisher Number:

99968910078