Polarity and Intensity: the Two Aspects of Sentiment Analysis

Leimin Tian, Catherine Lai, Johanna Moore


Abstract
Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often been overlooked. As a measurement of opinions and affective states, a sentiment score generally consists of two aspects: polarity and intensity. We decompose sentiment scores into these two aspects and study how they are conveyed through individual modalities and combined multimodal models in a naturalistic monologue setting. In particular, we build unimodal and multimodal multi-task learning models with sentiment score prediction as the main task and polarity and/or intensity classification as the auxiliary tasks. Our experiments show that sentiment analysis benefits from multi-task learning, and individual modalities differ when conveying the polarity and intensity aspects of sentiment.
Anthology ID:
W18-3306
Volume:
Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
40–47
Language:
URL:
https://aclanthology.org/W18-3306
DOI:
10.18653/v1/W18-3306
Bibkey:
Cite (ACL):
Leimin Tian, Catherine Lai, and Johanna Moore. 2018. Polarity and Intensity: the Two Aspects of Sentiment Analysis. In Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML), pages 40–47, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Polarity and Intensity: the Two Aspects of Sentiment Analysis (Tian et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-3306.pdf