Summary: Integrating Qualitative and Computational Text Analysis
In the light of recent research debates on computational social science and Digital Humanities (DH) as meanwhile adolescent disciplines dealing with big data (Reichert, 2014), I strove for answering in which ways Text Mining (TM) applications are able to support Qualitative Data Analysis (QDA) in the social sciences in a manner that fruitfully integrates a qualitative with a quantitative perspective. The guiding assumption was, the more modern Natural Language Processing (NLP) and Machine Learning (ML) algorithms enable us to identify patterns of `meaning’ from global contexts of mass data collections, while at the same time preserving opportunities to retrieve identified patterns again in local contexts of single documents, the more they allow for a fruitful integration of qualitative and quantitative text analysis. By combining extraction of qualitative knowledge from text to buttress understanding of social reality with quantification of extracted knowledge structures to infer on their relevancy, utilizing TM for QDA is inherently a mixed method research design.
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