Bidirectional LSTM Deep Model for Online Doctor Reviews Polarity Detection

Abstract

Online medical reviews contain patients' subjective evaluations and reflect their satisfaction with the treatment process and doctors. Mining and analysis of sentiment expressed in these medical data may be vital for different applications including adverse drug effects detection, doctor recommendation, and healthcare quality assessment. Nevertheless, medical sentiment analysis is a challenging and complex task because patients who write the reviews are usually non-professional users and tend to use informal language. The problem is more challenging in the Persian language due to its resource scarcity and complex structure. In this study, we introduce PODOR, a Persian dataset of online doctor reviews extracted from social web. Also, we propose a deep model based on the bidirectional long short-term memory for polarity detection of PODOR reviews. To show the effectiveness and suitability of the proposed model, we compared the model with six traditional supervised machine learning methods and three deep models. Preliminary comparative results indicated that our model outperformed traditional methods by 8% and 7%, and deep models by 2% and 3% in terms of accuracy and f1-measure.