Abstract

Sentiment analysis has been increasingly popular in the present digital era which attempts to analyse the consumer reviews acquired from websites, blogs and social media platforms. In hospitals and other healthcare organizations, understanding patient feedback helps to exceed in providing top-notch care. Sentiment analysis to enhance patient care is the way to know how patients feel about different service aspects, including processes, infrastructure, treatment, and healthcare professionals. Enhancing healthcare with sentiment analysis means removing human bias through consistent analysis, gaining real-time insights about patient satisfaction, and improving standards of care by incorporating patient feedback. The proposed work is structured into four phases to ensure a systematic approach to sentiment analysis using deep learning and NLP techniques. Each phase is designed to enhance data processing, feature extraction, feature selection, and sentiment classification, ensuring accurate and interpretable sentiment analysis in the healthcare domain. The proposed framework improves accuracy, interpretability, and feature selection while addressing key challenges in healthcare sentiment classification. This work will contribute significantly to the fields of Natural Language Processing, Healthcare AI, and Sentiment Analysis.

Authors

S. Punithavathy, J.M. Dhayashankar
Sri Ramakrishna Mission Vidyalaya College of Arts and Science, India

Keywords

Sentiment Analysis, Healthcare, Feedback, Feature Extraction, Feature Selection