Text Mining for Qualitative Data Analysis in the Social Sciences

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Gregor Wiedemann evaluates text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600, 000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing.Contents• Qualitative Data Analysis in a Digital World• Computer-Assisted Text Analysis in the Social Sciences• Integrating Text Mining Applications for Complex Analysis• Democratic Demarcation in Germany• V-TM – A Methodological Framework for Social Sciences• Integrating Qualitative and Computational Text AnalysisTarget Groups• Researchers and students in the fields of social sciences, digital humanities and communication science, scientists interested in innovative text analysis methods, computer scientists in interdisciplinary projects or research fields working with large amounts of textual data The AuthorGregor Wiedemann holds a doctoral degree from Leipzig University, Germany. He is the coordinator of the discipline-specific working groups in the CLARIN-D project, which develops a European virtual research infrastructure for digital language data analysis in the social sciences and humanities.