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Domain Adversarial Training for Aspect-Based Sentiment Analysis

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Web Information Systems Engineering – WISE 2022 (WISE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13724))

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Abstract

The continuously expanding digital possibilities, increasing number of social media platforms, and growing interest of companies in online marketing increase the importance of Aspect-Based Sentiment Analysis (ABSA). ABSA focuses on predicting the sentiment of an aspect in a text. In an ideal scenario, we would have labeled data for every existing domain, but acquiring annotated training data is costly. Transfer learning resolves this issue by building models that can be employed in different domains. The proposed work extends the state-of-the-art LCR-Rot-hop++ model for ABSA with the methodology of Domain Adversarial Training (DAT) in order to create a deep learning adaptable cross-domain structure, called the DAT-LCR-Rot-hop++. The major advantage of the DAT-LCR-Rot-hop++ is the fact that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging from 37% up until 77%, showing both the limitations and benefits of this approach. Once DAT is able to find the similarities between domains, it produces good results, but if the domains are too distant, it is not capable of generating domain-invariant features.

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Author information

Authors and Affiliations

  1. Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, The Netherlands

    Joris Knoester & Flavius Frasincar

  2. Bucharest University of Economic Studies, 010374, Bucharest, Romania

    Maria Mihaela Truşcǎ

Authors
  1. Joris Knoester
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  2. Flavius Frasincar
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  3. Maria Mihaela Truşcǎ
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Corresponding author

Correspondence to Maria Mihaela Truşcǎ .

Editor information

Editors and Affiliations

  1. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  2. The University of Queensland, Brisbane, QLD, Australia

    Helen Huang

  3. Sapienza Università di Roma, Rome, Italy

    Fabrizio Silvestri

  4. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  5. The New Cyber Research Department, Peng Cheng Laboratory, Shenzhen, China

    Yanchun Zhang

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Knoester, J., Frasincar, F., Truşcǎ, M.M. (2022). Domain Adversarial Training for Aspect-Based Sentiment Analysis. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-20891-1_3

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