Contextual Sentiment Analysis Transforms the Interpretation of Big Data in Healthcare

The widespread adoption of digital communication in healthcare, along with the growing number of mHealth applications, is generating vast amounts of data. Big data analytics can potentially transform the healthcare sector by analyzing diverse online medical texts. As a specialized NLP task, sentiment analysis aims to identify and extract sentiment from textual data using various techniques, such as machine learning and lexicon-based approaches. This paper explores sentiment polarity in online medical discourse, examining how contextual factors, such as inferred sentiment, sarcasm, irony and negation, affect sentiment interpretation. The research highlights the importance of pragmatic and semantic analysis in medical sentiment classification and proposes improved approaches for handling sentiment polarity in nuanced medical discourse. Insights gained from sentiment analysis can improve patient outcomes and satisfaction, support personalized care, and reduce healthcare costs.