@inproceedings{tarkka-etal-2024-automated,
    title = "Automated Emotion Annotation of {F}innish Parliamentary Speeches Using {GPT}-4",
    author = "Tarkka, Otto  and
      Koljonen, Jaakko  and
      Korhonen, Markus  and
      Laine, Juuso  and
      Martiskainen, Kristian  and
      Elo, Kimmo  and
      Laippala, Veronika",
    editor = "Fiser, Darja  and
      Eskevich, Maria  and
      Bordon, David",
    booktitle = "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",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.parlaclarin-1.11",
    pages = "70--76",
    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.",
}

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.
Tarkka, Otto Koljonen, Jaakko Korhonen, Markus Laine, Juuso Martiskainen, Kristian Elo, Kimmo Laippala, Veronika
Automated Emotion Annotation of Finnish Parliamentary Speeches Using GPT-4