Developing a Machine Learning Model to Categorize Mental Health Forums Using Scraping and Crawling in Python

Abstract

Mental health forums serve as invaluable online communities where individuals struggling with mental health problems find solace, support, and valuable resources. These platforms offer a unique space where young people can openly discuss their struggles, seek guidance from moderators and fellow users, and receive vital assistance. Within these forums, it is not uncommon to encounter posts that contain severe content, indicating that the user is in acute distress and may be at risk of self-harm. Research conducted through inductive thematic analysis highlights that while forums cannot replace the role of a trained counselor or therapist, they fulfill a critical role in providing young people with essential, lower-level support requirements. Participants in these forums have consistently reported them to be supportive environments where they feel comfortable sharing their experiences, offering advice, and asking questions. This sense of community makes individuals feel less isolated and more connected to others who understand their struggles. Our current project uses the power of machine learning to enhance the functionality of these mental health forums. We aim to develop a sophisticated model capable of automatically categorizing posts and discussions enabling more efficient navigation and targeted assistance. To accomplish this, we used web scraping and crawling techniques to gather data from diverse mental health forums. This collected data will serve as the foundation for training our machine-learning model to categorize forum posts into relevant mental health topics. This project promises to provide a valuable tool for both forum users seeking specific information and mental health professionals looking to offer precise and targeted support. Ultimately, our project strives to bolster the effectiveness of these forums as vital resources in the journey toward better mental well-being