@inproceedings{poria-etal-2017-context,
title = "Context-Dependent Sentiment Analysis in User-Generated Videos",
author = "Poria, Soujanya and
Cambria, Erik and
Hazarika, Devamanyu and
Majumder, Navonil and
Zadeh, Amir and
Morency, Louis-Philippe",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1081",
doi = "10.18653/v1/P17-1081",
pages = "873--883",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Context-Dependent Sentiment Analysis in User-Generated Videos
%A Poria, Soujanya
%A Cambria, Erik
%A Hazarika, Devamanyu
%A Majumder, Navonil
%A Zadeh, Amir
%A Morency, Louis-Philippe
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F poria-etal-2017-context
%X 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.
%R 10.18653/v1/P17-1081
%U https://aclanthology.org/P17-1081
%U https://doi.org/10.18653/v1/P17-1081
%P 873-883
Markdown (Informal)
[Context-Dependent Sentiment Analysis in User-Generated Videos](https://aclanthology.org/P17-1081) (Poria et al., ACL 2017)
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.