Stress Detection in Social Interactions Using NLP and Machine Learning

Most social media information is utilized for sentiment analysis, reviews, viewpoints, and impact. The suggested approach looks at the posts and comments to determine a person's level of stress by extending sentiment and evaluating emotions. Large datasets of tweets can be subjected to sentiment analysis with machine learning techniques, deep learning models, and BERT for sentiment categorization. Additionally, Latent Dirichlet Allocation is utilized that gathers word groupings and similar phrases that most accurately represent a set of documents by scanning a collection of documents, identifying patterns of words and phrases amongst them, and gathering these data. These emotions can also be utilized to analyze stress and sadness effectively. Both the BERT model and other machine learning models demonstrate excellent detection accuracy. The insights gained from this research contribute significantly to improving mental health. It is evident from the results that the trained model can identify the emotional state based on social interactions when analyzed using a variety of macro and micro variables.