Context-Dependent Sentiment Analysis in User-Generated Videos
Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, Louis-Philippe Morency
Abstract
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.- Anthology ID:
- P17-1081
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 873–883
- Language:
- URL:
- https://aclanthology.org/P17-1081
- DOI:
- 10.18653/v1/P17-1081
- Cite (ACL):
- Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, and Louis-Philippe Morency. 2017. Context-Dependent Sentiment Analysis in User-Generated Videos. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 873–883, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Context-Dependent Sentiment Analysis in User-Generated Videos (Poria et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P17-1081.pdf
- Code
- senticnet/sc-lstm + additional community code
- Data
- CPED, IEMOCAP, MELD, Multimodal Opinionlevel Sentiment Intensity