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Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model

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Abstract

User-generated content on healthcare web forums, particularly drug reviews, provides valuable information on drug benefits, effectiveness, side effects, dosage, condition, cost, and overall experiences. Applying Aspect-Based Sentiment Analysis (ABSA) can help researchers categorize sentiments toward specific aspects such as drug effectiveness, side effects, and treatment experiences. These insights are highly useful for healthcare professionals, pharmaceutical companies, and researchers to assess drug efficacy and safety, utilizing the vast amount of healthcare-related user-generated content available online. However, due to scarcity of annotated data for training ABSA models in the medical domain poses challenges in accurately extracting aspect terms. Also, the identification of implicit aspects poses a huge challenge as they frequently lack explicit names or keywords that directly indicate their presence. The domain-dependent nature of ABSA and the variability of term meanings across domains necessitate the incorporation of contextual information and semantic patterns. Therefore, we propose a novel model called Multi-task Learning based Dual Bidirectional LSTM Model (MLDBM) for ABSA of drug reviews. The MLDBM leverages BERT and incorporates a multi-head self-attention mechanism to produce aspect-specific representations which are further processed through the dual BiLSTM model. This enables the model to capture and analyze sentiments related to different aspects discussed in the reviews. We also introduce various modifications to the MLDBM to identify the constraints of the proposed model. The proposed model outperforms state-of-the-art models by achieving a performance gain of 8% to 12% on two benchmark datasets, demonstrating its effectiveness when compared to various baseline models. ABSA applied to drug reviews contributes to enhancing healthcare quality by considering different aspects of drugs as shared by consumers.

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Data availability

The datasets analyzed during the current study are available on reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Netaji Subhas University of Technology East Campus (erstwhile A.I.A.C.T.R.), Guru Gobind Singh Indraprastha University, Delhi, India

    Somiya Rani

  2. Department of Computer Science and Engineering, Netaji Subhas University of Technology, Delhi, India

    Amita Jain

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  1. Somiya Rani
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Rani, S., Jain, A. Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model. Multimed Tools Appl 83, 22473–22501 (2024). https://doi.org/10.1007/s11042-023-16360-3

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