Drugs Rating Generation and Recommendation from Sentiment Analysis of Drug Reviews using Machine Learning

A recommendation system can assist the user to compose an understanding of requirements and propose informed decisions from a lot of complicated knowledge. Recommendation from an analysis of sentiments seems to be a great challenge as user-generated content is represented using human language in several complicated ways. Many studies have focused on common fields such as reviews of electrical items, films, and restaurants, but not enough on health and medical issues. Sentiment analysis of healthcare in general and that of the drug experiences of individuals, in particular, may shed considerable light on how to focus on improving public health and reach the correct decision. In this paper, we design and implement a drug recommender system framework that applies sentiment analysis technologies on drug reviews. The objective of this research is to build a decision-making support platform to help patients to achieve more significant choices in drug selection. Firstly, we propose a sentimental measurement approach to drug reviews and generate ratings on drugs. Secondly, we take how much the drug reviews are useful to users, patient's conditions, and dictionary sentiment polarity of drug reviews into consideration. Then, we fuse those factors into the recommendation system to list appropriate medications. Experiments have been carried out using Decision Tree, K-Nearest Neighbors, and Linear Support Vector Classifier algorithm in rating generation and Hybrid model in recommendation based on the given open dataset. The analysis is carried out to tune the parameters for each algorithm in order to achieve greater performance. Finally, Linear Support Vector Classifier is selected for rating generation to obtain a good trade-off among model accuracy, model efficiency, and model scalability.