Review on Multi-lingual Sentiment Analysis in Health Care

Abstract

Multilingual sentiment analysis in healthcare is rapidly expanding, utilizing machine learning methods to identify emotions and sentiments in material written in several languages. This multidisciplinary field integrates computational linguistics, natural language processing, and health informatics to help healthcare providers better comprehend patient attitudes. Sentiment analysis is particularly useful in the healthcare industry since it facilitates comprehension of patient feedback, responses to interventions, and general contentment. Moreover, in an increasingly interconnected society, it can assist in recognizing the emotional states and concerns of patients from diverse linguistic origins. Healthcare providers can improve care and service by gaining insights into patient experiences through the analysis of patient reviews, social media posts, and other kinds of feedback in several languages. Sentiment analysis, for example, can be used to track patients' mental health over time and identify symptoms of depression or anxiety based on their interactions. These applications are becoming increasingly important for adjusting patient support and care, fostering better patient-provider communication, and eventually improving health outcomes. There are difficulties when implementing sentiment analysis in a multilingual setting, such as the requirement for extensive datasets in several languages and models that are sensitive to cultural quirks and context. By offering a foundation for creating more precise and sophisticated sentiment analysis systems that can function in a variety of linguistic and cultural contexts, advances in AI models, such as BERT and GPT variations, are assisting in addressing these issues. Recall that although sentiment analysis holds great potential, its use in healthcare needs to be done carefully to protect patient privacy and take ethical considerations into account. Sentiment analysis in healthcare can also assist in identifying unfulfilled medical and emotional demands of long-term patients, supporting patient-centred care models. In general, the incorporation of multilingual sentiment analysis into healthcare presents a multitude of opportunities and represents a promising facet of artificial intelligence's potential to enhance patient care outcomes and experiences.