A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques

Nowadays, the development of new computer-based technologies has led to rapid increase in the volume of user-generated textual content on the website. Patient-written medical and health-care reviews are among the most valuable and useful textual content on social media which have not been studied extensively by researchers in the fields of natural language processing (NLP) and data mining. These reviews offer insights into the interaction of patients with doctors, treatment, and their satisfaction or frustration with the delivery of healthcare services. In this study, we propose two deep fusion models based on three-way decision theory to analyze the drug reviews. The first fusion model, 3-way fusion of one deep model with a traditional learning algorithm (3W1DT) developed using a deep learning method as a primary classifier and a traditional learning method as the secondary method that is used when the confidence of the deep method during classification of test samples is low. In the second proposed deep fusion model, 3-way fusion of three deep models with a traditional model (3W3DT), three deep and one traditional models are trained on the entire training data and each classifies the test sample individually. Then, the most confident classifier is selected to classify the test drug review. Our results on the reviews based on Drugs.com dataset show that both proposed 3W1DT and 3W3DT methods outperformed the traditional and deep learning methods by 4% and the 3W3DT outperformed 3W1DT by 2% in terms of accuracy and F1-measure.