Sentiment Analysis in Healthcare: Leveraging Natural Language Processing for Patient Feedback and Mental Health Assessment
Abstract
Natural Language Processing (NLP) has enabled healthcare professionals to analyze patient-generated text data from surveys, social media, and electronic health records (EHRs) for insights into patient satisfaction and mental health conditions. This paper reviews sentiment analysis techniques, including lexicon-based approaches, deep learning models, and transformer-based architectures such as BERT for detecting emotional cues in text. The study highlights applications in early detection of depression, monitoring public health sentiments, and evaluating hospital performance. Ethical considerations, model biases, and future improvements in sentiment analysis for healthcare are also discussed.
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