The Application of Sentiment Analysis in Mental Health Monitoring and Support
Keywords:
Machine learning, Mental health, Mental wellbeing, Natural language processing, Sentiment analysisAbstract
The ability of sentiment analysis, a branch of Natural Language Processing (NLP), to assess psychological states and emotional states from textual data has drawn a lot of interest recently. An overview of sentiment analysis's use in the field of mental health is given in this publication. Through an overview of sentiment analysis's many approaches, instruments, and strategies, the study emphasizes how important it is to comprehending and tracking mental health. The study investigates the use of sentiment analysis, namely in online platforms, social media, and therapeutic contexts, to detect emotional markers of mental health conditions as stress, anxiety, and depression. It also talks about the difficulties of applying sentiment analysis in this delicate field, such as issues with accuracy, data privacy, and ethical considerations. Through case studies and current applications, the paper emphasizes the growing role of sentiment analysis in mental health support, offering new opportunities for real-time monitoring and personalized interventions. Finally, the paper concludes by addressing future directions and the potential of sentiment analysis to contribute to improving mental wellbeing through advanced AI technologies.
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