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@article{162593,
        author = {Srinath R and Arpitha Vasudev and Krishnaveni Navalgund and Kiran R S},
        title = {Review paper of drug recommendation system},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {458-462},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162593},
        abstract = {This study offers a brand-new approach to medication recommendation that is based on user-generated review sentiment analysis. By analysing the sentiment expressed in these evaluations, our method leverages machine learning techniques to increase the accuracy of medication recommendations. We use algorithms for sentiment categorization and natural language processing to interpret user sentiments in order to offer a more nuanced understanding of medication experiences.
As part of our process, we gather a wide range of drug reviews and use a strong sentiment analysis model. The system's ability to incorporate aspects like sentiment polarity, user feedback, and contextual data allows it to pick up on minute details in user encounters. By utilising machine learning algorithms, our system becomes capable of learning and adjusting to changing drug review patterns, which guarantees its efficacy in constantly changing healthcare environments .This is  suggested system for medicine recommendation.  
},
        keywords = {},
        month = {},
        }