Submit your paper : editorIJETjournal@gmail.com Paper Title : Drug Recommendation System based on Sentiment Analysis of Drug Reviews ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.7296042 MLA Style: -Mr.G. Prabhakar, A.Sahith,A.Rishika, Ch.Aishwarya Drug Recommendation System based on Sentiment Analysis of Drug Reviews , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -Mr.G. Prabhakar, A.Sahith,A.Rishika, Ch.Aishwarya Drug Recommendation System based on Sentiment Analysis of Drug Reviews , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract Since the coronavirus arose, there has been an unprecedented demand on legitimate clinical budgets due to a lack of specialists, caregivers, the right tools and treatments, etc. The entire medical profession is in pain, which contributes to a range of living things passing away. Because they were frustrated, people started using drugs frequently without having relevant conversations, which worsened their health conditions. There is a rise in creative labour for robotization, and machine literacy has grown more crucial in a range of tasks. With the help of this investigation, a medicine recommendation system that drastically reduces the requirement for specialists will be presented. In this analysis, perfection, recall, FL score, delicacy, and FTO score were used to assess the predicted sentiments. We frequently suggest taking a medication. Reference 1. Telemedicine, https://www.mohfw.gov.in/pdf/Telemedic ine.pdf 2. Wittich CM, Burkle CM, Lanier WL. Medication errors: an overview for clinicians. Mayo Clin Proc. 2014 Aug;89(8):1116-25. 3. CHEN, M. R., & WANG, H. F. (2013). The reason and prevention of hospital medication errors. Practical Journal of Clinical Medicine, 4. 4. Drug Review Dataset, https://archive.ics.uci.edu/ml/datasets/Dru g% 2BReview%2BDataset%2B%2528Drugs. com%2529# 5. Fox, Susannah, and Maeve Duggan. ”Health online 2013. 2013.” URL: http://pewinternet.org/Reports/2013/Healt h-online.aspx 6. Bartlett JG, Dowell SF, Mandell LA, File TM Jr, Musher DM, Fine MJ. Practice guidelines for the management of community-acquired pneumonia in adults. Infectious Diseases Society of America. Clin Infect Dis. 2000 Aug;31(2):347-82. doi: 10.1086/313954. Epub 2000 Sep 7. PMID: 10987697; PMCID: PMC7109923. 7. Fox, Susannah & Duggan, Maeve. (2012). Health Online 2013. Pew Research Internet Project Report. 8. T. N. Tekade and M. Emmanuel, ”Probabilistic aspect mining approach for interpretation and evaluation of drug reviews,” 2016 International Conference on Signal Processing, Communication, Power and Embed- ded System (SCOPES), Paralakhemundi, 2016, pp. 1471-1476, doi: 10.1109/SCOPES.2016.7955684. 9. Doulaverakis, C., Nikolaidis, G., Kleontas, A. et al. GalenOWL: Ontology-based drug recommendations discovery. J Biomed Semant 3, 14 (2012). https://doi.org/10.1186/2041-1480-3-14 10. Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong, and YanmingXie. 2016. Data- driven Automatic Treatment Regimen Development and Recommendation. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16). Association for Computing Machinery, New York, NY, USA, 1865–1874. DOI:https://doi.org/10.1145/2939672.2 939866 Keywords — Drug, Recommender System, Machine Learning, NLP, Smote, Bow, TF-IDF, Word2Vec, Sentiment analysis |