@InProceedings{tian-lai-moore:2018:W18-33,
  author    = {Tian, Leimin  and  Lai, Catherine  and  Moore, Johanna},
  title     = {Polarity and Intensity: the Two Aspects of Sentiment Analysis},
  booktitle = {Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {40--47},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-3306}
}