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<p>As the volume of information exchanged on the Internet has increased, Aspect-Based Sentiment Analysis (ABSA) has become an indispensable task for addressing the needs of various areas that rely on the exploitation of public opinion. This task first involves extracting the different aspects (e.g., price, quality) of an entity (e.g., laptops) and then assigning sentiment polarity (e.g., positive, negative) to them. In this paper, we focus exclusively on the Aspect Extraction (AE) task, which is the most critical component of ABSA. First, we provide a comprehensive overview of the AE task, which involves identifying aspects from text. Subsequently, due to the significant impact of datasets on the performance of aspect extraction models, a statistical analysis is performed on various datasets created for the AE task, considering factors such as availability, sources, and more. then, we thoroughly review the studies in this field, classifying them into four main approaches: linguistic knowledge-based approach, machine learning-based approach, deep learning-based approach, and hybrid approach. Finally, we evaluate the strengths and weaknesses of the proposed approaches, providing insights for researchers to stay informed about the latest advancements in this area. For future work, we explore the challenges associated with AE and provide potential directions for future research to further advance the field.</p>

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A Survey on Aspect Extraction: Approaches and Datasets

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Sarsabene Hammi,
  • Souha Mezghani Hammami,
  • Lamia Hadrich Belguith

摘要

As the volume of information exchanged on the Internet has increased, Aspect-Based Sentiment Analysis (ABSA) has become an indispensable task for addressing the needs of various areas that rely on the exploitation of public opinion. This task first involves extracting the different aspects (e.g., price, quality) of an entity (e.g., laptops) and then assigning sentiment polarity (e.g., positive, negative) to them. In this paper, we focus exclusively on the Aspect Extraction (AE) task, which is the most critical component of ABSA. First, we provide a comprehensive overview of the AE task, which involves identifying aspects from text. Subsequently, due to the significant impact of datasets on the performance of aspect extraction models, a statistical analysis is performed on various datasets created for the AE task, considering factors such as availability, sources, and more. then, we thoroughly review the studies in this field, classifying them into four main approaches: linguistic knowledge-based approach, machine learning-based approach, deep learning-based approach, and hybrid approach. Finally, we evaluate the strengths and weaknesses of the proposed approaches, providing insights for researchers to stay informed about the latest advancements in this area. For future work, we explore the challenges associated with AE and provide potential directions for future research to further advance the field.