Early Mental Health Detection and Emotional States in Teenagers Through Chatbot Systems Using Natural Language Processing (NLP)

The World Health Organization estimates that between 10% and 20% of young people worldwide suffer from mental health problems during adolescence, which is a crucial time for many youngsters. Adolescent mental health cases have increased by 31% as a result of the COVID-19 epidemic, which has made the issue much worse. Due to stigma, restricted access, and challenges in evaluating emotional states, traditional mental health care frequently faces challenges. The goal of this research is to create a chatbot system that uses machine learning and natural language processing (NLP) to identify emotional distress and mental health issues in teenagers at an early age. The study offers a safe environment for teenagers to express their emotions while examining chatbot interactions to find trends suggestive of mental health problems. To improve the efficacy of the chatbot, the methodology integrates quantitative and qualitative techniques, utilizing data from open datasets and mental health services. Sentiment analysis and emotion identification are important strategies that enable the chatbot to react sympathetically. By offering easily available support and highlighting the role of technology in addressing adolescent mental health issues, this project ultimately seeks to enhance youth mental health outcomes.