Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling

Philipp Grandeit, Carolyn Haberkern, Maximiliane Lang, Jens Albrecht, Robert Lehmann


Abstract
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.
Anthology ID:
2020.nlpcss-1.2
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–23
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpcss-1.2
DOI:
10.18653/v1/2020.nlpcss-1.2
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpcss-1.2.pdf