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A Cross-Domain Aspect-Based Sentiment Classification by Masking the Domain-Specific Words

Published: 07 June 2023 Publication History

Abstract

The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). It uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose a domain-dependent threshold value for deciding whether a word is domain-specific or not. For a target domain that is highly similar to the source domain, we find that a moderate attention threshold yields the best performance, while a target domain that is dissimilar requires a high attention threshold. Also, we observe information loss when we impose a too strict restriction and classify a small proportion of words as domain-independent.

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Cited By

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  • (2024)Cross-domain aspect-based sentiment classification with hybrid promptExpert Systems with Applications10.1016/j.eswa.2024.124680255(124680)Online publication date: Dec-2024
  • (2024)Comprehensive review and comparative analysis of transformer models in sentiment analysisKnowledge and Information Systems10.1007/s10115-024-02214-366:12(7305-7361)Online publication date: 1-Dec-2024
  • (2023)DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment ClassificationACM SIGAPP Applied Computing Review10.1145/3626307.362630923:3(19-31)Online publication date: 29-Sep-2023
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        cover image ACM Conferences
        SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
        March 2023
        1932 pages
        ISBN:9781450395175
        DOI:10.1145/3555776
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        Published: 07 June 2023

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        Author Tags

        1. ABSC
        2. cross-domain ABSC
        3. domain-specific word masking
        4. attention threshold
        5. DIWS-LCR-Rot-hop++

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        View all
        • (2024)Cross-domain aspect-based sentiment classification with hybrid promptExpert Systems with Applications10.1016/j.eswa.2024.124680255(124680)Online publication date: Dec-2024
        • (2024)Comprehensive review and comparative analysis of transformer models in sentiment analysisKnowledge and Information Systems10.1007/s10115-024-02214-366:12(7305-7361)Online publication date: 1-Dec-2024
        • (2023)DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment ClassificationACM SIGAPP Applied Computing Review10.1145/3626307.362630923:3(19-31)Online publication date: 29-Sep-2023
        • (2023)A mutual mean teacher framework for cross-domain aspect-based sentiment analysisThe Journal of Supercomputing10.1007/s11227-023-05792-180:7(9073-9095)Online publication date: 2-Dec-2023

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