FRIENDZONE: Sentiment Analysis and Support on Social Media

In the age of social media, individuals frequently share their emotional struggles, including feelings of anxiety, depression, and loneliness on these platforms. However, these posts often go unnoticed due to the overwhelming volume of content and the lack of efficient systems to detect and address emotional distress. Platforms like Facebook, Twitter, and Instagram primarily rely on manual reporting or user intervention, which are inadequate in providing timely support. Many users who express their struggles are left feeling isolated, as their posts fail to attract the necessary attention or empathy from their social circles. This can exacerbate their emotional challenges, potentially leading to more severe mental health crises. Without early detection and intervention, these users are at risk of falling through the cracks, as social media platforms lack the necessary tools to offer personalized support. This project aims to develop a sentiment-based chatbot system for social media applications to address emotional struggles expressed by users. Utilizing advanced natural language processing and sentiment analysis, the system will automatically monitor public posts and initiate supportive conversations when negative sentiment is detected. The chatbot will offer encouragement, empathy, and information on seeking help, including helpline numbers and contact details for mental health professionals. By proactively reaching out to users in distress, the system can provide timely emotional support, reduce feelings of isolation, and foster a sense of community. This innovative approach contributes to the broader conversation about technology’s role in addressing mental health issues, enhancing the emotional support capabilities of social media platforms and positively impacting users’ mental resilience.