Zusammenfassung
Die Durchführung einer Topic-Modell-Analyse setzt die Schätzung und Einstellung von Parametern voraus, die das Topic-Modell optimieren, d.h. die dazu führen, dass das Topic-Modell möglichst gut auf die Wortmengen der Dokumente passt. Dieses Schätzen und Optimieren der Parameter ist als das beschrieben, was in der Literatur das Lernen von Topic-Modellen genannt wird. Das Lernen von Topic-Modellen stützt sich auf Theorien der Statistik und der linearen Algebra. Die im Gebiet des maschinellen Lernens entwickelten Bayesschen Modelle wie Latent Dirichlet Allocation (LDA) und die in der linearen Algebra entwickelte Non-Negative-Matrix-Factorization (NMF) wurden zu ähnlichen Zwecken als Topic-Modelle vorgeschlagen. Beide Topic-Modelle werden heute benutzt werden, um Textquellen zu analysieren. Schließlich behandeln wir die Fragen der Evaluation und der Interpretation von Topic-Modellen. Wir beschreiben ebenfalls drei mögliche Umsetzungen des Analyseprozesses durch Skript-Programmierung mit R und python, und mittels der interaktiven Web-Applikation TopicExplorer.
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Papilloud, C., Hinneburg, A. (2018). Durchführung von Topic-Modell-Analysen. In: Qualitative Textanalyse mit Topic-Modellen. Studienskripten zur Soziologie. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-21980-2_3
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