Emotion Mining: A Framework for Depression Detection

Depression is a widespread mental health issue, and the continuous rise in mental health issues has fueled our search for new and innovative detection methods. Sentiment analysis combined with machine learning is a scalable and efficient approach to detect and categorize human emotions from text taken from sources such as social media. This paper reviews recent progress in depression detection using sentiment analysis and machine learning models, with emphasis on various steps in the process – data extraction, data preprocessing, feature selection, model training, and sentiment categorization. The main machine learning methodologies, challenges faced by researchers, and the future direction are discussed, we have focused on existing studies that utilized both traditional machine learning algorithms and advanced deep learning algorithms such as BERT and LSTM. This may be used for early mental health issue detection, continuous mental health monitoring, and personalized interventions for a more impactful treatment by analyzing textual data. This is a new and accessible approach that would improve access to mental health care reduce the stigma surrounding it and improve clinical decision making but providing additional screening methods using automated systems.