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BL
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| Record Number
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606036
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| Doc. No
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b435255
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| Main Entry
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Wiedemann, Gregor,1983-
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| Title & Author
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Text Mining for Qualitative Data Analysis in the Social Sciences : : A Study on Democratic Discourse in Germany.
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| Series Statement
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Kritische Studien zur Demokratie
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| Page. NO
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1 online resource (307 pages).
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| ISBN
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9783658153090
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: 3658153091
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3658153083
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9783658153083
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| Notes
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Description based upon print version of record
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| Contents
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Preface; Contents; List of Figures; List of Tables; List of Abbreviations; 1. Introduction: Qualitative Data Analysis in a Digital World; 1.1. The Emergence of "Digital Humanities"; 1.2. Digital Text and Social Science Research; 1.3. Example Study: Research Question and Data Set; 1.3.1. Democratic Demarcation; 1.3.2. Data Set; 1.4. Contributions and Structure of the Study; 2. Computer-Assisted Text Analysis in the Social Sciences; 2.1. Text as Data between Quality and Quantity; 2.2. Text as Data for Natural Language Processing; 2.2.1. Modeling Semantics; 2.2.2. Linguistic Preprocessing
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2.2.3. Text Mining Applications2.3. Types of Computational Qualitative Data Analysis; 2.3.1. Computational Content Analysis; 2.3.2. Computer-Assisted Qualitative Data Analysis; 2.3.3. Lexicometrics for Corpus Exploration; 2.3.4. Machine Learning; 3. Integrating Text Mining Applications for Complex Analysis; 3.1. Document Retrieval; 3.1.1. Requirements; 3.1.2. Key Term Extraction; 3.1.3. Retrieval with Dictionaries; 3.1.4. Contextualizing Dictionaries; 3.1.5. Scoring Co-Occurrences; 3.1.6. Evaluation; 3.1.7. Summary of Lessons Learned; 3.2. Corpus Exploration; 3.2.1. Requirements
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3.2.2. Identification and Evaluation of Topics3.2.3. Clustering of Time Periods; 3.2.4. Selection of Topics; 3.2.5. Term Co-Occurrences; 3.2.6. Keyness of Terms; 3.2.7. Sentiments of Key Terms; 3.2.8. Semantically Enriched Co-Occurrence Graphs; 3.2.9. Summary of Lessons Learned; 3.3. Classification for Qualitative Data Analysis; 3.3.1. Requirements; 3.3.2. Experimental Data; 3.3.3. Individual Classification; 3.3.4. Training Set Size and Semantic Smoothing; 3.3.5. Classification for Proportion and Trend Analysis; 3.3.6. Active Learning; 3.3.7. Summary of Lessons Learned
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4. Exemplary Study: Democratic Demarcation in Germany4.1. Democratic Demarcation; 4.2. Exploration; 4.2.1. Democratic Demarcation from 1950-1956; 4.2.2. Democratic Demarcation from 1957-1970; 4.2.3. Democratic Demarcation from 1971-1988; 4.2.4. Democratic Demarcation from 1989-2000; 4.2.5. Democratic Demarcation from 2001-2011; 4.3. Classification of Demarcation Statements; 4.3.1. Category System; 4.3.2. Supervised Active Learning of Categories; 4.3.3. Category Trends and Co-Occurrences; 4.4. Conclusions and Further Analyses; 5. V-TM -- A Methodological Framework for Social Science
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5.1. Requirements5.1.1. Data Management; 5.1.2. Goals of Analysis; 5.2. Workflow Design; 5.2.1. Overview; 5.2.2. Workflows; 5.3. Result Integration and Documentation; 5.3.1. Integration; 5.3.2. Documentation; 5.4. Methodological Integration; 6. Summary: Integrating Qualitative and Computational Text Analysis; 6.1. Meeting Requirements; 6.2. Exemplary Study; 6.3. Methodological Systematization; 6.4. Further Developments; A. Data Tables, Graphs and Algorithms; Bibliography
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| Subject
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Natural language processing (Computer science)
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| Subject
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Data mining-- Germany.
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| Subject
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Social sciences-- Data processing.
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| Subject
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Computer science.
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| Dewey Classification
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006.35
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| LC Classification
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QA76.9.N38
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| Added Entry
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Ohio Library and Information Network.
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