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Deep Learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper

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Intelligent Systems and Pattern Recognition (ISPR 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1941))

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Abstract

Due to the significant increase in the volume of data shared on the web, Aspect-Based Sentiment Analysis (ABSA) has become essential. This task ensures a detailed sentiment analysis. It identifies firstly the aspect terms (e.g., price, food, etc.) and then classifies their sentiment polarity as positive, negative, or neutral. Many approaches have been used to treat this task including the machine learning-based approach, the rule-based approach, etc. However, with the important increase in the content of the internet, these approaches became relatively unable to analyze this volume of information, resulting in the emergence of the deep learning-based approach which is the subfield of the machine learning-based approach.

Recently many researchers used the deep learning-based approach to address the ABSA. This paper provides a summary of the deep learning models that have been developed for ABSA, as well as a survey of studies that have employed these models to address different subtasks of the ABSA task. Finally, we discuss the implications of our work and potential avenues for future research.

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Authors and Affiliations

  1. ANLP Research Group, MIRACL Lab, FSEGS, University of Sfax, Sfax, Tunisia

    Sarsabene Hammi, Souha Mezghani Hammami & Lamia Hadrich Belguith

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  1. Sarsabene Hammi
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  2. Souha Mezghani Hammami
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  3. Lamia Hadrich Belguith
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Correspondence to Sarsabene Hammi , Souha Mezghani Hammami or Lamia Hadrich Belguith .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Akram Bennour

  2. Sharjah University, Sharjah, United Arab Emirates

    Ahmed Bouridane

  3. University of Toulouse, Toulouse, France

    Lotfi Chaari

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Hammi, S., Hammami, S.M., Belguith, L.H. (2024). Deep Learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper. In: Bennour, A., Bouridane, A., Chaari, L. (eds) Intelligent Systems and Pattern Recognition. ISPR 2023. Communications in Computer and Information Science, vol 1941. Springer, Cham. https://doi.org/10.1007/978-3-031-46338-9_13

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  • DOI: https://doi.org/10.1007/978-3-031-46338-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46337-2

  • Online ISBN: 978-3-031-46338-9

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