Polarity and intensity: the two aspects of sentiment analysis

Leimin Tian, Catherine Lai, Johanna D. Moore

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


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 multitask 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.
Original languageEnglish
Title of host publicationACL 2018 - First Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)
Subtitle of host publicationProceedings of the Workshop - July 20, 2018 Melbourne, Australia
EditorsAmir Zadeh, Louis-Philippe Morency, Paul Pu Liang, Soujanya Poria, Erik Cambria, Stefan Scherer
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages8
ISBN (Electronic)9781948087469
Publication statusPublished - 2018
Externally publishedYes
EventGrand Challenge and Workshopon Human Multimodal Language 2018 - Melbourne, Australia
Duration: 20 Jul 201820 Jul 2018


ConferenceGrand Challenge and Workshopon Human Multimodal Language 2018
Abbreviated titleChallenge-HML 2018
Internet address

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