@inproceedings{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",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.640/",
doi = "10.63317/59khdy9uv6uk",
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."
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%0 Conference Proceedings
%T A Japanese Dataset for Aspect-based Sentiment Polarity Classification and Emotion Intensity Estimation
%A Hanafusa, Kentaro
%A Manabe, Kota
%A Maeda, Yuki
%A Maekawa, Daisuke
%A Kajiwara, Tomoyuki
%A Hayashi, Hideaki
%A Nakashima, Yuta
%A Nagahara, Hajime
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F hanafusa-etal-2026-japanese
%X 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.
%R 10.63317/59khdy9uv6uk
%U https://aclanthology.org/2026.lrec-1.640/
%U https://doi.org/10.63317/59khdy9uv6uk
%P 8076-8084
Markdown (Informal)
[A Japanese Dataset for Aspect-based Sentiment Polarity Classification and Emotion Intensity Estimation](https://aclanthology.org/2026.lrec-1.640/) (Hanafusa et al., LREC 2026)
ACL
- Kentaro Hanafusa, Kota Manabe, Yuki Maeda, Daisuke Maekawa, Tomoyuki Kajiwara, Hideaki Hayashi, Yuta Nakashima, and Hajime Nagahara. 2026. A Japanese Dataset for Aspect-based Sentiment Polarity Classification and Emotion Intensity Estimation. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 8076–8084, Palma de Mallorca, Spain. ELRA Language Resource Association.