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    <name type="personal">
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            <namePart type="given">Amir</namePart>
            <namePart type="family">Zadeh</namePart>
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        <name type="personal">
            <namePart type="given">Paul</namePart>
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            <namePart type="given">Soujanya</namePart>
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        <name type="personal">
            <namePart type="given">Erik</namePart>
            <namePart type="family">Cambria</namePart>
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            <namePart type="given">Stefan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Polarity and Intensity: the Two Aspects of Sentiment Analysis
%A Tian, Leimin
%A Lai, Catherine
%A Moore, Johanna
%Y Zadeh, Amir
%Y Liang, Paul Pu
%Y Morency, Louis-Philippe
%Y Poria, Soujanya
%Y Cambria, Erik
%Y Scherer, Stefan
%S Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F tian-etal-2018-polarity
%X 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.
%R 10.18653/v1/W18-3306
%U https://aclanthology.org/W18-3306
%U https://doi.org/10.18653/v1/W18-3306
%P 40-47

Markdown (Informal)

[Polarity and Intensity: the Two Aspects of Sentiment Analysis](https://aclanthology.org/W18-3306) (Tian et al., ACL 2018)

• Polarity and Intensity: the Two Aspects of Sentiment Analysis (Tian et al., ACL 2018)

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.