Improving aspect-based neural sentiment classification with lexicon enhancement, attention regularization and sentiment induction

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• dc.contributor.author Bao, Lingxian
• dc.contributor.author Lambert, Patrik
• dc.contributor.author Badia Cardus, Toni
• dc.date.accessioned 2023-03-17T07:19:45Z
• dc.date.available 2023-03-17T07:19:45Z
• dc.date.issued 2024
• dc.description.abstract Deep neural networks as an end-to-end approach lack robustness from an application point of view, as it is very difficult to fix an obvious problem without retraining the model, for example, when a model consistently predicts positive when seeing the word “terrible.” Meanwhile, it is less stressed that the commonly used attention mechanism is likely to “over-fit” by being overly sparse, so that some key positions in the input sequence could be overlooked by the network. To address these problems, we proposed a lexicon-enhanced attention LSTM model in 2019, named ATLX. In this paper, we describe extended experiments and analysis of the ATLX model. And, we also try to further improve the aspect-based sentiment analysis system by combining a vector-based sentiment domain adaptation method.
• dc.format.mimetype application/pdf
• dc.identifier.citation Bao L, Lambert P, Badia T. Improving aspect-based neural sentiment classification with lexicon enhancement, attention regularization and sentiment induction. Nat Lang Eng. 2024;30(1):30 p. DOI: 10.1017/S1351324922000432
• dc.identifier.doi http://dx.doi.org/10.1017/S1351324922000432
• dc.identifier.issn 1351-3249
• dc.identifier.uri http://hdl.handle.net/10230/56255
• dc.language.iso eng
• dc.publisher Cambridge University Press
• dc.relation.ispartof Natural Language Engineering. 2024;30(1):30 p.
• dc.rights © The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Sentiment Analysis
• dc.subject.keyword Deep Learning
• dc.subject.keyword Attention
• dc.subject.keyword Lexicon
• dc.subject.keyword Domain Adaptation
• dc.title Improving aspect-based neural sentiment classification with lexicon enhancement, attention regularization and sentiment induction
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion