Abstract: Healthcare is a critical component of the medical industry in the modern digital era, as consumers look for pertinent health information online. Although the internet is a great resource, consumers may find it difficult to get useful information due to the large amount of scattered clinical data across multiple websites. Sentiment analysis, machine learning, and natural language processing (NLP) are used by an advanced drug recommendation system to examine user opinions in drug-related content. After processing this data, machine learning algorithms customize recommendations based on user profiles and adjust to changing sentiments over time. In order to accurately evaluate sentiment in user evaluations, natural language processing (NLP) is essential for comprehending and contextualizing linguistic nuances. The amalgamation of quantitative and qualitative data yields highly customized and context-sensitive recommendations, thereby augmenting the user experience in its entirety. In this paper, a drug recommender system based on user-generated drug reviews sentiment analysis is presented. With an emphasis on filling the knowledge vacuum in sentiment analysis research related to healthcare, the system seeks to assist patients in choosing medications with knowledge.

Key words: Sentiment Analysis, Natural Language Processing(NLP), Machine learning

| DOI: 10.17148/IARJSET.2024.11523