Final Thesis: Text Mining for Relationship Extraction

Abstract: Qualitative Data Analysis (QDA) methods are based on manual coding of texts. To extract a domain model from a text corpus using QDA, information has to be extracted and compiled into the domain model by hand. This is especially a problem for cases where large amounts of data have to be analyzed. For this purpose, We present a relationship extraction approach based on Natural Language Processing. It automates the extraction of relationships between codes that were provided by the coder. This speeds up the analysis process and helps to uncover relationships the human coder might have missed. Our method produces a graphical overview of relationships that were found to exist between codes. It is evaluated by comparison with previously generated models from existing Qualitative Data Analysis projects.

Keywords: Information Retrieval, Text Mining, Natural Language Processing, Qualitative Data Analysis, QDA

PDFs: Master Thesis, Work Description

Reference: Martin Hofmann. Text Mining for Relationship Extraction. Master Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg: 2017.

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