Leveraging Sentiment Analysis of Drugs Review-Based Drugs Recommender System

The goal in this research was to transform unstructured textual data for drug datasets into structured texts while integrating sentiment analysis and recommendation systems to deviate from conventional recommendation systems once the sentiment feature was extracted from the rating. The procedure used in the research consists of multiple stages: the first involves using natural language processing techniques to prepare the data, and the second involves using classification models logistic regression findings to make predictions with a 90% prediction accuracy. The last major problem is creating drug recommendation suggestion lists utilizing two different scenarios: the first one uses the K-nearest neighbor, while the second one uses the cosine similarity.