There has been a growing recognition of mental health as an integral part of one's overall health. And various cases highlight the need for mental health support on a global scale.
In consideration of these mental health issues and people having difficulties coping with them, this web application will use the natural language processing model to analyze the user's mental health based on user input text and with the Chat GPT API we will provide recommendations to improve, enhance users mental health.
We used the MERN stack as our base tech stack, and we used pre-trained BERT which is a famous NLP model. We fine-tuned BERT with the Twitter Sentimental Analysis dataset to score texts based on their sentiments.
We struggled the most with formatting the data stored in our database from the website and the machine learning side. The format formed by the users had to match a specific format for our ML developer to train and test his model.
With the Natural Processing Language model, we have successfully detected a way to analyze the user's mental health state. Additionally, our goal was to provide a user-friendly UI, and as a result, we believe that the interface is easily accessible and noticeable which met our needs.
For the front-end developers, we learned how data is trained using the information we collect from our users. We also learned how to efficiently delegate tasks since there was a lot of workload for the UI/UX components.
For the ML developer, he learned the process of displaying the data that he generated with his model to the users. He was reminded of the importance of the front end for enhanced user experience.
In this time of economic instability, people often forget to take care of their mental health. As strong believers in the importance of our project, we are continuing to further improve our product and mental health for everyone. Specifically, we want to display more insightful data on our dashboard and make it more accessible to everyone through SEO. We also want to enhance our user interface for ease of use. Lastly, we want to improve our ML model by training with more datasets for generalizing the performance of the model.