Sentiment Analysis and Emotion Detection with Healthcare Perspective

Abstract

The health sector has benefited in many ways from the data science and artificial intelligence (AI). One of the most promising applications of data science and AI in the healthcare field is sentiment analysis and emotion detection. Sentiment analysis is the automatic categorization of sentiment in a free text, whereas emotional detection categorizes emotion on a human face using a sophisticated image dispensation. This chapter aimed to focus on Sentiment Analysis and Emotion detection applications related to wellbeing healthcare through a systematic review of the recent literature. With the support of AI methods and other mathematical models, sentiment analysis can offer significant assistance to healthcare professionals, especially psychiatrists to understand the mental health and psychological problems of wellbeing. In general, people with certain intolerable problems, serious illnesses, addictions to something, suicide victims, and caregivers use social networks, health websites, and other web portals to share their sentiments. These are important data sources for sentiment analysis related to health. Emotion detection and recognition mechanisms use facial expressions for emotions such as joy, sadness, surprise, and anger, and in addition, capture “micro-expressions” or controlled expression of body language as the main source of data. Analysis outcomes help health professionals to decide when patients need help or need medication. In conclusion, health professionals and community service volunteers or caregivers can use the results of the sentiment and emotion detection analysis to help with wellbeing when they need it. The accuracy of the analysis results can be improved by combining the analysis of human expressions from a variety of forms such as texts, facial expressions, body language, and speech.