Utilizing Deep Learning Methods for Stress Detection in Social Media Platform

As modern communication increasingly relies on SNS (social networking sites), new possibilities have emerged to explore and address mental health problems. By applying NLP with deep learning for stress detection, this study presents a novel approach for addressing mental health problems before they deteriorate, helping in decreasing loads of mental health diseases. Thus, it has been observed that Deep learning algorithms are capable of predicting stress faster and more precise way. This research is specifically concerned with deep learning approaches with stress identification in the manifestation of stress in the language used by Twitter users. The study includes data collecting, preprocessing, and the creation of a stress detection model. The study examines the model's performance across two datasets: “Politician Tweets” and “Common User Tweets.”. The LSTM model demonstrated its efficiency in identifying stress-related content from non-stress content. The accuracy of datasets is 81.54% and 85.43%, indicating how well the model adapts to diverse scenarios. In addition, our findings bring out the significance of social media as channels for early detection of stress, thus inviting early diagnosis of possible mental health diseases. Demonstrate that technology can help address the issue of inadequate mental health resources, especially in countries like Pakistan, where professional psychologists are scarce. Our study contributes to establishing a feasible stress detection framework appropriate for resource-constrained environments by leveraging data-driven approaches and focusing on specific issues Pakistan faces. This research not only demonstrates the potential strength of deep learning in stress detection but also lays the groundwork for future research into ways of using technology for societal benefit.