Smart Drug Recommendation System Using Sentiment Analysis of Drug Reviews
Prof. Dr. S D N Hayath Ali · International Journal for Research in Applied Science and Engineering Technology · 2025
Global healthcare disruptions have precipitated extraordinary challenges within medical service delivery systems, culminating in severe healthcare professional shortages and compromised patient access to qualified therapeutic consultation. These circumstances have fostered widespread autonomous medication selection behaviors, frequently resulting in inappropriate or potentially detrimental treatment protocols. Our investigation presents a novel therapeutic advisory framework that harnesses user experience analytics through sophisticated computational intelligence methodologies to furnish personalized and trustworthy pharmaceutical guidance. Our innovative approach transcends conventional computational learning paradigms by establishing a comprehensive digital consultation platform that exhibits therapeutic options alongside exhaustive performance analytics and integrated patient insight compilation. We exploited the extensive Drugs.com user experience database, implementing cutting-edge textual processing methodologies encompassing Term Frequency-Inverse Document Frequency computation, Word2Vec semantic modeling techniques, and sophisticated attribute extraction protocols. Our research examined multiple computational approaches including Logistic Regression modeling, Random Forest classification frameworks, Naive Bayes probabilistic models, and Support Vector Machine architectures for emotional content analysis. The optimized TF-IDF methodology coupled with Linear Support Vector Classification generated outstanding results, attaining 93% classification accuracy. Our user-centric interface exhibits pharmaceutical options through innovative card-based presentations, featuring therapeutic names, performance indicators, comprehensive descriptions, and balanced summaries highlighting therapeutic benefits alongside potential adverse effects. This groundbreaking design amplifies user comprehension while facilitating evidence-based decision-making rooted in authentic patient testimonials, providing crucial support for patients and healthcare providers, especially within geographically isolated regions experiencing healthcare access limitations.