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Deep learning for aspect-based sentiment analysis: a review.

Linan Zhu1, Minhao Xu1, Yinwei Bao1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.

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|September 12, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning advances aspect-based sentiment analysis (ABSA) for extracting insights from user content. This overview details ABSA frameworks, challenges, and novel relation considerations for improved decision-making.

Keywords:
Aspect-based sentiment analysisDeep learningMulti-task learningRelation extraction

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Data Science

Background:

  • Explosive growth of user-generated content (UGC) offers valuable decision-making data.
  • Extracting accurate information from massive UGC datasets remains a significant challenge.
  • Sentiment analysis is crucial for identifying opinions towards specific targets within UGC.

Purpose of the Study:

  • To provide a comprehensive overview of deep learning applications in aspect-based sentiment analysis (ABSA).
  • To introduce the fundamental task of ABSA and its significance in understanding user opinions.
  • To explore novel aspects of ABSA, including inter-object relations.

Main Methods:

  • Introduction to the core concepts and task definition of ABSA.
  • Presentation of the ABSA framework from subtask and modeling perspectives.
  • Discussion of challenges and future directions in ABSA research.

Main Results:

  • Deep learning offers powerful techniques for tackling the complexities of ABSA.
  • The article outlines key subtasks and modeling approaches within the ABSA framework.
  • Identified challenges and proposed areas for future research in sentiment analysis.

Conclusions:

  • Deep learning is pivotal for advancing aspect-based sentiment analysis.
  • Understanding ABSA frameworks and challenges is essential for effective UGC analysis.
  • Incorporating inter-object relations presents a promising avenue for future ABSA research.