Mohammad Rostami, Digbalay Bose, Shrikanth Narayanan, and Aram Galstyan. Domain Adaptation for Sentiment Analysis Using Robust Internal Representations. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 11484–11498, Association for Computational Linguistics, Singapore, December 2023.

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Abstract

Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their customers to improve their products and to determine optimal marketing strategies. Due to the existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention. These methods mitigate the domain gap between different applications by training cross-domain generalizable classifiers which relax the need for data annotation for each domain. We develop a domain adaptation method which induces large margins between data representations that belong to different classes in an embedding space. This embedding space is trained to be domain-agnostic by matching the data distributions across the domains. Large interclass margins in the source domain help to reduce the effect of “domain shift” in the target domain. Theoretical and empirical analysis are provided to demonstrate that the proposed method is effective.

BibTeX Entry

@inproceedings{rostami-etal-2023-domain,
    title = "Domain Adaptation for Sentiment Analysis Using Robust Internal Representations",
    author = "Rostami, Mohammad  and
      Bose, Digbalay  and
      Narayanan, Shrikanth  and
      Galstyan, Aram",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.769",
    doi = "10.18653/v1/2023.findings-emnlp.769",
    pages = "11484--11498",
    abstract = "Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their customers to improve their products and to determine optimal marketing strategies. Due to the existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention. These methods mitigate the domain gap between different applications by training cross-domain generalizable classifiers which relax the need for data annotation for each domain. We develop a domain adaptation method which induces large margins between data representations that belong to different classes in an embedding space. This embedding space is trained to be domain-agnostic by matching the data distributions across the domains. Large interclass margins in the source domain help to reduce the effect of {``}domain shift{''} in the target domain. Theoretical and empirical analysis are provided to demonstrate that the proposed method is effective.",
}

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