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
- Editors:
- Amir Zadeh, Paul Pu Liang, Louis-Philippe Morency, Soujanya Poria, Erik Cambria, Stefan Scherer
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 40–47
- Language:
- URL:
- https://aclanthology.org/W18-3306
- DOI:
- 10.18653/v1/W18-3306
- 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., ACL 2018)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W18-3306.pdf