• PRAVEENA A
• Boyilla.Vamsi
• Jagarlamudi Arish Babu
• Cherukuri Harish
• Chelikam Gayathri Reddy

A recommendation system helps the user understand needs and provides guidelines when making decisions out of a sea of confusing information. Since that user-generated data is expressed in a variety of complex ways using human language, generating suggestions from a study of attitudes appears to be a challenging task. Healthcare sentiment analysis is less focused on making informed judgments and less engaged in raising the bar for public health. In this project, we create and put into use a drug recommendation system that analyzes drug reviews using sentiment analysis technology. The goal of this research is to create a system for making decisions that will enable patients to choose from a vast array of medications. In the initial stage of our development, we provide an approach to sentimental measurement for medicine reviews and offer ratings on medications. In addition, we analyze the dictionary sentiment, polarity of medicine reviews, patient conditions, and how accurate the reviews are useful to users. Next, to identify suitable drugs, we incorporate those variables into the recommendation algorithm. Convolution neural networks have been tested in experiments for recommendation based on the provided open dataset. To increase performance, analysis is done to fine-tune the settings for each method. In order to achieve good trade-offs between model accuracy, model efficiency, and model scalability, Linear Support Vector Classifier is improved in rating generation.

Drug Recommendation

"DRUGS RATING GENERATION AND RECOMMENDATION FROM SENTIMENT ANALYSIS OF DRUG REVIEWS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.j244-j250, April-2024, Available :http://www.jetir.org/papers/JETIR2404932.pdf

"DRUGS RATING GENERATION AND RECOMMENDATION FROM SENTIMENT ANALYSIS OF DRUG REVIEWS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppj244-j250, April-2024, Available at : http://www.jetir.org/papers/JETIR2404932.pdf