Automated Emotion Annotation of Finnish Parliamentary Speeches Using GPT-4

Otto Tarkka, Jaakko Koljonen, Markus Korhonen, Juuso Laine, Kristian Martiskainen, Kimmo Elo, Veronika Laippala


Abstract
In this paper, we test the efficacy of using GPT-4 to annotate a dataset that is the used to train a BERT classifier for emotion analysis. Manual data annotation is often a laborious and expensive task and emotion annotation, specifically, has proved difficult even for expert annotators. We show that using GPT-4 can produce equally good results as doing data annotation manually while saving a lot of time and money. We train a BERT classifier on our automatically annotated dataset and get results that outperform a BERT classifier that is trained on machine translated data. Our paper shows how Large Language Models can be used to work with and analyse parliamentary corpora.
Anthology ID:
2024.parlaclarin-1.11
Volume:
Proceedings of the IV Workshop on Creating, Analysing, and Increasing Accessibility of Parliamentary Corpora (ParlaCLARIN) @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Darja Fiser, Maria Eskevich, David Bordon
Venues:
ParlaCLARIN | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
70–76
Language:
URL:
https://aclanthology.org/2024.parlaclarin-1.11
DOI:
Bibkey:
Cite (ACL):
Otto Tarkka, Jaakko Koljonen, Markus Korhonen, Juuso Laine, Kristian Martiskainen, Kimmo Elo, and Veronika Laippala. 2024. Automated Emotion Annotation of Finnish Parliamentary Speeches Using GPT-4. In Proceedings of the IV Workshop on Creating, Analysing, and Increasing Accessibility of Parliamentary Corpora (ParlaCLARIN) @ LREC-COLING 2024, pages 70–76, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Automated Emotion Annotation of Finnish Parliamentary Speeches Using GPT-4 (Tarkka et al., ParlaCLARIN-WS 2024)
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https://preview.aclanthology.org/landing_page/2024.parlaclarin-1.11.pdf