Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174419,
        author = {Narayana Swami Putta and Mr . R Venkatesh and Ms. K Soujanya and Ms. M Himaja and Mr . E Kundan Surya},
        title = {Sentiment-Based Drug Recommendation System with Patient Experience Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3920-3923},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174419},
        abstract = {Selecting the right medication is a critical yet challenging decision that affects millions of patients worldwide. Traditional drug selection methods often prioritize clinical trials and expert opinions, overlooking valuable insights from real patient experiences.
To address this gap, an advanced drug recommendation system is developed that leverages the VADER sentiment analyzer to interpret patient-generated reviews. Multiple factors—including treatment effectiveness, side effects, overall satisfaction, and review usefulness—are combined through a weighted scoring algorithm to generate condition-specific recommendations.
An intuitive interface then displays comprehensive metrics, such as average ratings, sentiment-based effectiveness scores, potential side effect risks, and representative patient reviews, offering clear guidance to both healthcare providers and patients.
Experimental results indicate that integrating sentiment analysis with quantitative metrics significantly enhances the accuracy and reliability of medication recommendations, ultimately supporting more informed and patient-centered decision-making.
By transforming patient feedback into actionable insights, this approach underscores the potential of sentiment-driven systems to improve treatment outcomes and elevate the quality of healthcare services.},
        keywords = {Sentiment Analysis, Drug Recommendation, VADER, Patient Feedback, Weighted Scoring, Fuzzy Matching, Side Effects, Effectiveness},
        month = {March},
        }