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sEntIMeldCL: Enhancing explicit knowledge via Uniform-based Implicit Contrastive Mechanism for Aspect-Level Sentiment

Khwaja Mutahir Ahmad1, Qiao Liu2, Abdullah Aman Khan3

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610097, PR China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 61170, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces sEntIMeldCL-ALSA, a novel approach for aspect-level sentiment analysis (ALSA) that improves accuracy by considering semantic correlations and handling negations. The model enhances explicit knowledge for ALSA, achieving state-of-the-art results on benchmark datasets.

Keywords:
Aspect term extractionAspect-level sentiment analysisDiscourse segmentationImplicit data augmentationSupervised contrastive learning

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Aspect-level sentiment analysis (ALSA) is crucial for fine-grained text understanding.
  • Existing ALSA models often overlook negation impacts and struggle with multi-aspect semantic representation.
  • Current augmentation methods using word substitution may not fully capture semantic nuances.

Purpose of the Study:

  • To propose a Uniform-based Implicit Contrastive Mechanism (sEntIMeldCL-ALSA) to enhance explicit knowledge for ALSA.
  • To address limitations in handling negations and representing multiple aspects within sentences.
  • To improve the extraction of semantic correlations between aspect terms and sentence polarity.

Main Methods:

  • Developed a three-module model: aspect-specific segmentation adapter, uniform-based implicit augmentation, and a dual contrastive loss (ExImp contrastive module).
  • Integrated explicit and implicit representations into a unified semantic representation.
  • Employed data augmentation techniques focused on polarity-dependent sentences.

Main Results:

  • The proposed sEntIMeldCL model achieved state-of-the-art performance on three out of four benchmark datasets.
  • Demonstrated significant improvements in accuracy, reaching up to 87.37% on the Restaurant dataset.
  • Showcased enhanced F1 scores, with improvements up to 84.69% on the MAMS dataset.

Conclusions:

  • The sEntIMeldCL-ALSA model effectively enhances explicit knowledge for aspect-level sentiment analysis.
  • The proposed method successfully captures semantic correlations and improves handling of complex linguistic phenomena like negation.
  • Results confirm the model's superiority in extracting aspect-specific sentiment, outperforming existing methods on key datasets.