An Innovative Method for Stress Detection in Social Network Messages Based on GA and DT-SVM Model

Many mental health illnesses can affect a person's mood, behavior, emotions, and thoughts. In terms of public health around the world, this is a big deal. An attempt is being made to examine social media posts for indicators of stress and depressive tendencies; this should help with the early detection of mental health illnesses. Preprocessing the model text, feature extraction, and training make up the three phases described by the method. Tokenization, stemming, data standardization, emoji translation, and stop word removal are some of the procedures involved in text pre-processing. In this proposed it how to extract features for ocular validation using principal component analysis (PCA) and latent Dirichlet Allocation (LDA). A GA-DT-SVM was used to train the model. The proposed approach works better than GA and SVM, with an average accuracy of 92.07%.