Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling

  • Qualitative content analysis is a systematic method commonly used in the social sciences to analyze textual data from interviews or online discussions. However, this method usually requires high expertise and manual effort because human coders need to read, interpret, and manually annotate text passages. This is especially true if the system of categories used for annotation is complex and semantically rich. Therefore, qualitative content analysis could benefit greatly from automated coding. In this work, we investigate the usage of machine learning-based text classification models for automatic coding in the area of psycho-social online counseling. We developed a system of over 50 categories to analyze counseling conversations, labeled over 10.000 text passages manually, and evaluated the performance of different machine learning-based classifiers against human coders.

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Metadaten
Author:Philipp Grandeit, Carolyn Haberkern, Maximiliane Lang, Jens AlbrechtORCiD, Robert LehmannORCiD
DOI:https://doi.org/10.18653/v1/2020.nlpcss-1.2
Parent Title (English):Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA, USA
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2020/11/20
Reviewed:Begutachtet/Reviewed
Release Date:2024/07/29
Pagenumber:13
First Page:11
Last Page:23
Konferenzangabe:Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
institutes:Institut für e-Beratung
Fakultät Informatik
Fakultät Sozialwissenschaften
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Soziale & ökonomische Transformation
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