• Amar Singh
• Bhargav Ayare
• Vanshika Shetty
• Jaya Jeswani

Against the backdrop of issues made worse by things like the COVID-19 epidemic and a shortage of physicians in rural compared to metropolitan regions, the importance of customized drug recommendations keeps getting bigger. Preprocessing of the data, sentiment analysis using methods like TF-IDF vectorization, and classification algorithms like CNN, KNN, and Linear Support Vector Classifier are all part of the suggested methodology. Sentiment analysis algorithms that use patient reviews to identify side effects, effectiveness of medications, and patient happiness empower both patients and practitioners. This article explains the promising potential of sentiment-based medication recommendation systems by thoroughly reviewing the body of existing research and methodology. In the rapidly developing field of digital healthcare, the combination of sentiment analysis with machine learning provides a revolutionary method for enhancing drug choice, enhancing patient outcomes, and advancing healthcare decision-making.

Drug Recommendation, Drug Reviews, Sentiment Analysis, CNN, KNN, Feature Extraction.

"Drug Recommendation Based On Sentiment Analysis Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.b548-b553, March-2024, Available :http://www.jetir.org/papers/JETIR2403165.pdf

"Drug Recommendation Based On Sentiment Analysis Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppb548-b553, March-2024, Available at : http://www.jetir.org/papers/JETIR2403165.pdf