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@article{178509,
        author = {Kashish Verma and Parth Chugh and Lavi Kaushik and Mashrufa Mondal},
        title = {Medicine Recommendation System based on Sentiment Analysis of Medicine Reviews using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4468-4474},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178509},
        abstract = {The healthcare industry is increasingly adopting intelligent systems to improve drug and disease recommendation processes, ensuring timely and accurate support for both patients and healthcare providers. This paper introduces a dual-function Drug and Disease Recommendation System that integrates sentiment analysis of drug reviews and symptom-based disease prediction using machine learning techniques. The system utilizes patient feedback to assess drug efficacy through sentiment polarity classification, while also mapping user-reported symptoms to probable diseases. Implemented as a real-time multilingual Web application, the system employs Support Vector Machine with TF-IDF vectorization for sentiment analysis (96.35% accuracy) and Multi-Layer Perceptron with TF-IDF for disease prediction (Jaccard Score: 0.7631), offering PDF output and REST API support for seamless integration and usability.},
        keywords = {},
        month = {May},
        }