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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.
Chong et al. (Fri,) studied this question.