Toward Comprehensive Understanding of a Sentiment Based on Human Motives

Naoki Otani, Eduard Hovy


Abstract
In sentiment detection, the natural language processing community has focused on determining holders, facets, and valences, but has paid little attention to the reasons for sentiment decisions. Our work considers human motives as the driver for human sentiments and addresses the problem of motive detection as the first step. Following a study in psychology, we define six basic motives that cover a wide range of topics appearing in review texts, annotate 1,600 texts in restaurant and laptop domains with the motives, and report the performance of baseline methods on this new dataset. We also show that cross-domain transfer learning boosts detection performance, which indicates that these universal motives exist across different domains.
Anthology ID:
P19-1461
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4672–4677
Language:
URL:
https://aclanthology.org/P19-1461
DOI:
10.18653/v1/P19-1461
Bibkey:
Cite (ACL):
Naoki Otani and Eduard Hovy. 2019. Toward Comprehensive Understanding of a Sentiment Based on Human Motives. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4672–4677, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Toward Comprehensive Understanding of a Sentiment Based on Human Motives (Otani & Hovy, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1461.pdf
Supplementary:
 P19-1461.Supplementary.pdf
Code
 notani/acl2019-human-motive-identification