Abstract: In today's digital age, healthcare is a crucial aspect of the medical field, with people seeking relevant health information online. Despite the internet being a valuable resource, the abundance of dispersed clinical data across various websites poses challenges for users in finding beneficial information. An advanced drug recommendation system uses sentiment analysis, machine learning, as well as NLP to analyze user sentiments in drug-related content. ML algorithms process this data, tailoring recommendations to individual profiles and adapting to evolving sentiments over time. NLP plays a crucial role in understanding and contextualizing language nuances in user reviews, ensuring precise sentiment interpretation. This combination of quantitative and qualitative data provides highly personalized, context-aware suggestions, enhancing the overall user experience and enhancing the overall user experience. This paper presents a drug recommender system that leverages sentiment analysis on user-generated drug reviews. Focused on addressing the gap in sentiment analysis research within the healthcare domain, the system aims to aid patients in making informed decisions regarding drug selection.

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

Cite:
Lakshmi K.K, Ananya P, Bhavanashree K S, Dharmavarapu Lakshmi Aaradhya, Sindhuja Chindirala, "A Survey on: Sentiment-Driven Medication Guidance", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 10, no. 12, pp. 118-123, 2023, Crossref https://doi.org/10.17148/IARJSET.2023.101215.

| DOI: 10.17148/IARJSET.2023.101215