Recommendation Tool for Alleviating Depression and Suicidal Tendencies Through Healthier Social Media Use

Each day, the rise of mental health issues due to social media use increases. Due to this, there is a strong need for solutions that help promote a healthier online experience. Our research aims to address this mental health concern. It is structured into two main components: sentiment analysis of social media videos and the development of a new recommendation tool that will reduce these mental health risks. The sentiment analysis component of the project leverages advanced machine learning models to classify social media content using music and select frames to categorize them as happy, neutral, or sad. The unique aspect of our research lies in the recommendation tool, which adjusts user exposure to sad content without user awareness, simulating real user interactions. This new approach is designed to promote a safer experience while online by mitigating the negative emotional impacts of social media.