@inproceedings{picca-pavlopoulos-2024-deciphering,
    title = "Deciphering Emotional Landscapes in the {I}liad: A Novel {F}rench-Annotated Dataset for Emotion Recognition",
    author = "Picca, Davide  and
      Pavlopoulos, John",
    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.399",
    pages = "4462--4467",
    abstract = "One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity{'}s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.",
}

One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity{'}s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.
Picca, Davide Pavlopoulos, John
Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition