@inproceedings{gao-etal-2024-enhancing-emotion,
    title = "Enhancing Emotion Prediction in News Headlines: Insights from {C}hat{GPT} and {S}eq2{S}eq Models for Free-Text Generation",
    author = "Gao, Ge  and
      Kim, Jongin  and
      Paik, Sejin  and
      Novozhilova, Ekaterina  and
      Liu, Yi  and
      Bonna, Sarah T.  and
      Betke, Margrit  and
      Wijaya, Derry Tanti",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.526",
    pages = "5944--5955",
    abstract = "Predicting emotions elicited by news headlines can be challenging as the task is largely influenced by the varying nature of people{'}s interpretations and backgrounds. Previous works have explored classifying discrete emotions directly from news headlines. We provide a different approach to tackling this problem by utilizing people{'}s explanations of their emotion, written in free-text, on how they feel after reading a news headline. Using the dataset BU-NEmo+ (Gao et al., 2022), we found that for emotion classification, the free-text explanations have a strong correlation with the dominant emotion elicited by the headlines. The free-text explanations also contain more sentimental context than the news headlines alone and can serve as a better input to emotion classification models. Therefore, in this work we explored generating emotion explanations from headlines by training a sequence-to-sequence transformer model and by using pretrained large language model, ChatGPT (GPT-4). We then used the generated emotion explanations for emotion classification. In addition, we also experimented with training the pretrained T5 model for the intermediate task of explanation generation before fine-tuning it for emotion classification. Using McNemar{'}s significance test, methods that incorporate GPT-generated free-text emotion explanations demonstrated significant improvement (P-value {\textless} 0.05) in emotion classification from headlines, compared to methods that only use headlines. This underscores the value of using intermediate free-text explanations for emotion prediction tasks with headlines.",
}

Predicting emotions elicited by news headlines can be challenging as the task is largely influenced by the varying nature of people{'}s interpretations and backgrounds. Previous works have explored classifying discrete emotions directly from news headlines. We provide a different approach to tackling this problem by utilizing people{'}s explanations of their emotion, written in free-text, on how they feel after reading a news headline. Using the dataset BU-NEmo+ (Gao et al., 2022), we found that for emotion classification, the free-text explanations have a strong correlation with the dominant emotion elicited by the headlines. The free-text explanations also contain more sentimental context than the news headlines alone and can serve as a better input to emotion classification models. Therefore, in this work we explored generating emotion explanations from headlines by training a sequence-to-sequence transformer model and by using pretrained large language model, ChatGPT (GPT-4). We then used the generated emotion explanations for emotion classification. In addition, we also experimented with training the pretrained T5 model for the intermediate task of explanation generation before fine-tuning it for emotion classification. Using McNemar{'}s significance test, methods that incorporate GPT-generated free-text emotion explanations demonstrated significant improvement (P-value {\textless} 0.05) in emotion classification from headlines, compared to methods that only use headlines. This underscores the value of using intermediate free-text explanations for emotion prediction tasks with headlines.
Gao, Ge Kim, Jongin Paik, Sejin Novozhilova, Ekaterina Liu, Yi Bonna, Sarah T. Betke, Margrit Wijaya, Derry Tanti
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation