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<p>As healthcare text data becomes increasingly complex, it is vital for sentiment analysis to capture local patterns and global contextual dependencies. In this paper, we propose a hybrid Swin Transformer–BiLSTM–Spatial MLP (Swin-MLP) model that leverages hierarchical attention, shifted-window mechanisms, and spatial MLP layers to extract features from domain-specific healthcare text better. The framework is tested on domain-specific datasets for Drug Review and Medical Text, and performance is assessed against baseline models (BERT, LSTM, and GRU). Our findings show that the Swin-MLP model performs significantly better overall, achieving superior metrics (accuracy, precision, recall, F1-score, and AUC) and improving mean accuracy by 1–2% over BERT. Statistical tests to assess significance (McNemar's test and paired t-test) indicate that improvements are statistically significant (p &lt; 0.05), suggesting the efficacy of the architectural innovations. The results' implications indicate that the model is robust, efficiently converges to classification, and is potentially helpful for a wide range of domain-specific sentiment analyses in healthcare. We will examine future research directions into exploring lightweight attention mechanisms, cross-domain multimodal sentiment analysis, federated learning to protect privacy, and hardware implications for rapid training and inference.</p>

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Leveraging Swin Transformer for advanced sentiment analysis: a new paradigm

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Gaurav Kumar Rajput,
  • Saurabh Kumar Srivastava,
  • Namit Gupta

摘要

As healthcare text data becomes increasingly complex, it is vital for sentiment analysis to capture local patterns and global contextual dependencies. In this paper, we propose a hybrid Swin Transformer–BiLSTM–Spatial MLP (Swin-MLP) model that leverages hierarchical attention, shifted-window mechanisms, and spatial MLP layers to extract features from domain-specific healthcare text better. The framework is tested on domain-specific datasets for Drug Review and Medical Text, and performance is assessed against baseline models (BERT, LSTM, and GRU). Our findings show that the Swin-MLP model performs significantly better overall, achieving superior metrics (accuracy, precision, recall, F1-score, and AUC) and improving mean accuracy by 1–2% over BERT. Statistical tests to assess significance (McNemar's test and paired t-test) indicate that improvements are statistically significant (p < 0.05), suggesting the efficacy of the architectural innovations. The results' implications indicate that the model is robust, efficiently converges to classification, and is potentially helpful for a wide range of domain-specific sentiment analyses in healthcare. We will examine future research directions into exploring lightweight attention mechanisms, cross-domain multimodal sentiment analysis, federated learning to protect privacy, and hardware implications for rapid training and inference.