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<p>The proliferation of online platforms has intensified the need for fine-grained sentiment analysis of customer reviews. While traditional Aspect-based Sentiment Analysis (ABSA) focuses on sentiment classification, it often overlooks the crucial distinction between subjective opinions and objective statements. This paper introduces the Subjectivity Multitask Aspect-based Sentiment Analysis framework, which integrates Subjectivity Detection as an auxiliary task with Aspect Sentiment Classification through a unified multitask learning architecture. The model leverages BERT embeddings, a Bidirectional LSTM, a self-attention mechanism, and a Neural Tensor Network to effectively model the interplay between subjectivity and sentiment. Crucially, our experimental results on the comprehensive MEMD multi-domain dataset demonstrate the framework’s effectiveness, achieving a state-of-the-art F1-score of 87.26%. This represents a significant 26.89 percentage point improvement over the previous baseline, showcasing superior performance in both precision-recall balance and cross-domain stability. Key contributions include: (1) manual annotation of aspect-level subjectivity labels for a large-scale multi-domain dataset, (2) development of an integrated multitask framework that addresses precision-recall imbalance in ABSA, and (3) empirical validation showing consistent improvements across diverse domains. Results confirm that joint optimization of related tasks enhances sentiment classification accuracy while maintaining computational efficiency.</p>

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Multitask Learning and BERT Embedding: A Comprehensive Approach to Subjectivity Detection and Aspect-Based Sentiment Analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Wing Kin Chong,
  • Hu Ng,
  • Timothy Tzen Vun Yap,
  • Ian K. T. Tan,
  • Vik Tor Goh,
  • Lai Kuan Wong,
  • Dong Theng Cher

摘要

The proliferation of online platforms has intensified the need for fine-grained sentiment analysis of customer reviews. While traditional Aspect-based Sentiment Analysis (ABSA) focuses on sentiment classification, it often overlooks the crucial distinction between subjective opinions and objective statements. This paper introduces the Subjectivity Multitask Aspect-based Sentiment Analysis framework, which integrates Subjectivity Detection as an auxiliary task with Aspect Sentiment Classification through a unified multitask learning architecture. The model leverages BERT embeddings, a Bidirectional LSTM, a self-attention mechanism, and a Neural Tensor Network to effectively model the interplay between subjectivity and sentiment. Crucially, our experimental results on the comprehensive MEMD multi-domain dataset demonstrate the framework’s effectiveness, achieving a state-of-the-art F1-score of 87.26%. This represents a significant 26.89 percentage point improvement over the previous baseline, showcasing superior performance in both precision-recall balance and cross-domain stability. Key contributions include: (1) manual annotation of aspect-level subjectivity labels for a large-scale multi-domain dataset, (2) development of an integrated multitask framework that addresses precision-recall imbalance in ABSA, and (3) empirical validation showing consistent improvements across diverse domains. Results confirm that joint optimization of related tasks enhances sentiment classification accuracy while maintaining computational efficiency.