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    <abstract>Large Language Models, such as ChatGPT, offer numerous possibilities and prospects for academic research. However, there has been a gap in empirical research regarding their utilisation as keyword extraction and classification tools in qualitative research; perspectives from the social sciences and humanities have been notably limited. Moreover, Finnish-language data have not been used in previous studies. In this article, I aim to address these gaps by providing insights into the utilisation of ChatGPT and drawing comparisons with a rule-based Natural Language Processing method called Etuma. I will focus on assessing the effectiveness of classification and the methods’ adherence to scientific principles. The findings of the study indicate that the classic recall and precision trade-off applies to the methods: ChatGPT’s precision is high, but its recall is comparatively low, while the results are the opposite for Etuma. I also discuss the implications of the results and outline ideas for leveraging the strengths of both methods in future studies.</abstract>
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%0 Conference Proceedings
%T Introducing ChatGPT to a researcher’s toolkit: An empirical comparison between rule-based and large language model approach in the context of qualitative content analysis of political texts in Finnish
%A Kousa, Ilona
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Pirinen, Flammie
%Y Alnajjar, Khalid
%Y Miyagawa, So
%Y Bizzoni, Yuri
%Y Partanen, Niko
%Y Rueter, Jack
%S Proceedings of the Joint 3rd International Conference on Natural Language Processing for Digital Humanities and 8th International Workshop on Computational Linguistics for Uralic Languages
%D 2023
%8 December
%I Association for Computational Linguistics
%C Tokyo, Japan
%F kousa-2023-introducing
%X Large Language Models, such as ChatGPT, offer numerous possibilities and prospects for academic research. However, there has been a gap in empirical research regarding their utilisation as keyword extraction and classification tools in qualitative research; perspectives from the social sciences and humanities have been notably limited. Moreover, Finnish-language data have not been used in previous studies. In this article, I aim to address these gaps by providing insights into the utilisation of ChatGPT and drawing comparisons with a rule-based Natural Language Processing method called Etuma. I will focus on assessing the effectiveness of classification and the methods’ adherence to scientific principles. The findings of the study indicate that the classic recall and precision trade-off applies to the methods: ChatGPT’s precision is high, but its recall is comparatively low, while the results are the opposite for Etuma. I also discuss the implications of the results and outline ideas for leveraging the strengths of both methods in future studies.
%U https://aclanthology.org/2023.nlp4dh-1.12
%P 102-113

Markdown (Informal)

[Introducing ChatGPT to a researcher’s toolkit: An empirical comparison between rule-based and large language model approach in the context of qualitative content analysis of political texts in Finnish](https://aclanthology.org/2023.nlp4dh-1.12) (Kousa, NLP4DH-IWCLUL 2023)

• Introducing ChatGPT to a researcher’s toolkit: An empirical comparison between rule-based and large language model approach in the context of qualitative content analysis of political texts in Finnish (Kousa, NLP4DH-IWCLUL 2023)

ACL

• Ilona Kousa. 2023. Introducing ChatGPT to a researcher’s toolkit: An empirical comparison between rule-based and large language model approach in the context of qualitative content analysis of political texts in Finnish. In Proceedings of the Joint 3rd International Conference on Natural Language Processing for Digital Humanities and 8th International Workshop on Computational Linguistics for Uralic Languages, pages 102–113, Tokyo, Japan. Association for Computational Linguistics.