Abstract
Sentiment Analysis (SA) of medical reviews is crucial for improving healthcare outcomes. However, analyzing sentiment in low-resource languages such as Asian Hindi presents significant challenges. In this study, we propose an automatic graph construction approach to extract relevant features from medical reviews in Asian Hindi languages. We explore different types of Long Short-Term Memory (LSTMs), including traditional LSTMs, bidirectional LSTMs, and attention-based LSTMs, to classify the sentiment of medical reviews. Our proposed approach uses attention-based LSTM architecture and pre-trained Word2Vec embeddings to achieve high accuracy. We compare the proposed approach with existing models using various evaluation metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that our proposed approach outperforms all existing models in terms of accuracy, achieving an accuracy score of 81%. These findings could have implications for improving healthcare outcomes by enabling better monitoring of patient feedback and identifying areas for improvement in medical services.
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