@article{hanafusa-etal-2026-japanese,
    title = "A {J}apanese Dataset for Aspect-based Sentiment Polarity Classification and Emotion Intensity Estimation",
    author = "Hanafusa, Kentaro  and
      Manabe, Kota  and
      Maeda, Yuki  and
      Maekawa, Daisuke  and
      Kajiwara, Tomoyuki  and
      Hayashi, Hideaki  and
      Nakashima, Yuta  and
      Nagahara, Hajime",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.640/",
    pages = "8076--8084",
    abstract = "We manually construct and publicly release a Japanese dataset for Aspect-based Sentiment Analysis{~}(ABSA), annotated with both sentiment polarity and the emotional intensities for Plutchik{'}s eight emotions. Existing datasets for Japanese ABSA only handle sentiment polarity classification. Therefore, we manually annotated Plutchik{'}s eight emotions with a four-point scale and sentiment polarity with a five-point scale to words in the Japanese sentiment analysis corpus WRIME. Analysis of this corpus revealed that word-level emotions more strongly reflect the reader{'}s objective impression than the writer{'}s subjective perspective. Furthermore, the results of evaluation experiments on word-level emotion estimation quantitatively demonstrated that while Large Language Models achieve high performance, they struggle with the estimation of the ``trust'' emotion. Additionally, we demonstrated that multi-task learning, utilizing both word and sentence levels, can improve performance on difficult-to-estimate subjective emotions."
}