Innovative Approaches to Mental Health: Chatbot Engagement and NLP for Detection and Support

Sentiment analysis is the technique of analyzing people’s emotions, attitudes, and thoughts expressed in the form of written text. Detection and addressing of mental health issues early is vital for better outcomes and reducing overall societal costs. The majority of the research is based on opinion mining to comprehend the public’s opinion toward product perception. This work introduces a novel chatbot system to interact with users. The gathered user data is classified using a hybrid model combining Bi-LSTM and Naïve Bayes classifier. The classifiers are trained on a concatenated dataset comprising reviews, tweets, comments, and texts, with additional comparison against various machine learning classifiers for accuracy assessment. Results indicate the effectiveness of the trained and tested model, achieving a high accuracy rate in predicting mental health conditions.