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A disentangled adversarial neural topic model for separating opinions from plots in user reviews
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Pergola, Gabriele, Gui, Lin and He, Yulan (2021) A disentangled adversarial neural topic model for separating opinions from plots in user reviews. In: 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Online, 6–11 Jun 2021. Published in: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies pp. 2870-2883.
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Official URL: https://www.aclweb.org/anthology/2021.naacl-main.2...
Abstract
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM). Although these approaches have achieved significant results, surprisingly very little work has been done on how to disentangle the latent topics. Existing topic models when applied to reviews may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions such as plot summaries in movie and book reviews. It is thus desirable to automatically separate opinion topics from plot/neutral ones enabling a better interpretability. In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. We conduct an extensive experimental assessment introducing a new collection of movie and book reviews paired with their plots, namely MOBO dataset, showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.
Item Type: | Conference Item (Paper) | ||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Artificial intelligence, Machine learning, Neural networks (Computer science), Data mining , Sentiment analysis | ||||||||||||
Journal or Publication Title: | Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | ||||||||||||
Publisher: | Association for Computational Linguistics | ||||||||||||
Official Date: | 2 June 2021 | ||||||||||||
Dates: |
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Page Range: | pp. 2870-2883 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 4 June 2021 | ||||||||||||
Date of first compliant Open Access: | 8 June 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||
Title of Event: | 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Online | ||||||||||||
Date(s) of Event: | 6–11 Jun 2021 | ||||||||||||
Open Access Version: |
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