WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
Abstract
We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer’s subjective labels than the readers’. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.- Anthology ID:
- 2021.naacl-main.169
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2095–2104
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.169
- DOI:
- 10.18653/v1/2021.naacl-main.169
- Cite (ACL):
- Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, and Hajime Nagahara. 2021. WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2095–2104, Online. Association for Computational Linguistics.
- Cite (Informal):
- WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations (Kajiwara et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.naacl-main.169.pdf
- Code
- ids-cv/wrime
- Data
- EmoBank, ISEAR