An Introduction to Text Mining

Gabe Ignatow is Professor of Sociology and Director of Graduate Studies at the University of North Texas. His research interests are mainly in the areas of sociological theory, digital research methods, cognitive social science, and the philosophy of social science. His most recent books are Text Mining and An Introduction to Text Mining, both coauthored with Rada Mihalcea (University of Michigan). He is also a coeditor, with Wayne Brekhus (University of Missouri), of the Oxford Handbook of Cognitive Sociology.Rada Mihalcea is a professor of computer science and engineering at the University of Michigan. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the following journals: Computational Linguistics, Language Resources and Evaluation, Natural Language Engineering, Research on Language and Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a general chair for the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL, 2015) and a program cochair for the Conference of the Association for Computational Linguistics (2011) and the Conference on Empirical Methods in Natural Language Processing (2009). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.

• AcknowledgmentsPrefaceNote to the ReaderAbout the AuthorsPART I. FOUNDATIONSChapter 1. Text Mining and Text AnalysisLearning ObjectivesIntroductionSix Approaches to Text AnalysisChallenges and Limitations of Using Online DataConclusionKey TermsHighlightsReview QuestionsDiscussion QuestionsDeveloping a Research ProposalFurther ReadingChapter 2. Acquiring DataLearning ObjectivesIntroductionOnline Data SourcesAdvantages and Limitations of Online Digital Resources for Social Science ResearchExamples of Social Science Research Using Digital DataConclusionKey TermHighlightsDiscussion QuestionsChapter 3. Research EthicsLearning ObjectivesIntroductionRespect for Persons, Beneficence, and JusticeEthical GuidelinesInstitutional Review BoardsPrivacyInformed ConsentManipulationPublishing EthicsConclusionKey TermsHighlightsReview QuestionsDiscussion QuestionsWeb ResourcesDeveloping a Research ProposalFurther ReadingChapter 4. The Philosophy and Logic of Text MiningLearning ObjectivesIntroductionOntological and Epistemological PositionsMetatheoryMaking InferencesConclusionKey TermsHighlightsDiscussion QuestionsInternet ResourcesDeveloping a Research ProposalFurther ReadingPART II. RESEARCH DESIGN AND BASIC TOOLSChapter 5. Designing Your Research ProjectLearning ObjectivesIntroductionCritical DecisionsIdiographic and Nomothetic ResearchLevels of AnalysisQualitative, Quantitative, and Mixed Methods ResearchChoosing DataFormatting Your DataConclusionKey TermsHighlightsReview QuestionsDiscussion QuestionsDeveloping a Research ProposalFurther ReadingChapter 6. Web Scraping and CrawlingLearning ObjectivesIntroductionWeb StatisticsWeb CrawlingWeb ScrapingSoftware for Web Crawling and ScrapingConclusionKey TermsHighlightsDiscussion QuestionsPART III. TEXT MINING FUNDAMENTALSChapter 7. Lexical ResourcesLearning ObjectivesIntroductionWordNetRoget’s ThesaurusLinguistic Inquiry and Word CountGeneral InquirerWikipediaConclusionKey TermsHighlightsDiscussion TopicsChapter 8. Basic Text ProcessingLearning ObjectivesIntroductionBasic Text ProcessingLanguage Models and Text StatisticsMore Advanced Text ProcessingConclusionKey TermsHighlightsDiscussion TopicsChapter 9. Supervised LearningLearning ObjectivesIntroductionFeature Representation and WeightingSupervised Learning AlgorithmsEvaluation of Supervised LearningConclusionKey TermsHighlightsDiscussion TopicsPART IV. TEXT ANALYSIS METHODS FROM THE HUMANITIES AND SOCIAL SCIENCESChapter 10. Analyzing NarrativesLearning ObjectivesIntroductionApproaches to Narrative AnalysisPlanning a Narrative Analysis Research ProjectQualitative Narrative AnalysisMixed Methods and Quantitative Narrative Analysis StudiesConclusionKey TermsHighlightsReview QuestionsDeveloping a Research ProposalFurther ReadingChapter 11. Analyzing ThemesLearning ObjectivesIntroductionHow to Analyze ThemesExamples of Thematic AnalysisConclusionKey TermsHighlightsReview QuestionsDeveloping a Research ProposalFurther ReadingChapter 12. Analyzing MetaphorsLearning ObjectivesIntroductionCognitive Metaphor TheoryApproaches to Metaphor AnalysisQualitative, Quantitative, and Mixed MethodsConclusionKey TermsHighlightsReview QuestionsDeveloping a Research ProposalFurther ReadingPART V. TEXT MINING METHODS FROM COMPUTER SCIENCEChapter 13. Text ClassificationLearning ObjectivesIntroductionWhat Is Text Classification?Applications of Text ClassificationApproaches to Text ClassificationConclusionKey TermsHighlightsDiscussion TopicsChapter 14. Opinion MiningLearning ObjectivesIntroductionWhat Is Opinion Mining?Resources for Opinion MiningApproaches to Opinion MiningConclusionKey TermsHighlightsDiscussion TopicsChapter 15. Information ExtractionLearning ObjectivesIntroductionEntity ExtractionRelation ExtractionWeb Information ExtractionTemplate FillingConclusionKey TermsHighlightsDiscussion TopicsChapter 16. Analyzing TopicsLearning ObjectivesIntroductionWhat Are Topic Models?How to Use Topic ModelsExamples of Topic ModelingConclusionKey TermsHighlightsReview QuestionsDeveloping a Research ProposalInternet ResourcesFurther ReadingPART VI. WRITING AND REPORTING YOUR RESEARCHChapter 17. Writing and Reporting Your ResearchLearning ObjectivesIntroduction: Academic WritingEvidence and TheoryThe Structure of Social Science Research PapersConclusionKey TermsHighlightsWeb ResourcesUndergraduate Research JournalsFurther ReadingAppendix A. Data Sources for Text MiningAppendix B. Text Preparation and Cleaning SoftwareAppendix C. General Text Analysis SoftwareAppendix D. Qualitative Data Analysis SoftwareAppendix E. Opinion Mining SoftwareAppendix F. Concordance and Keyword Frequency SoftwareAppendix G. Visualization SoftwareAppendix H. List of WebsitesAppendix I. Statistical ToolsGlossaryReferencesIndex

"This is a comprehensive book on a timely and important research method for social scientific research. Researchers who want to learn the development of text mining methods and learn how to integrate the methods into their research projects will find this book beneficial."