Detection of Stress Over Social Interactions Using Machine Learning and Natural Language Processing

In the field of natural language processing, the investigation of stress is turning out to be increasingly critical. The advancement of recommender frameworks and computerized emotional well-being assessments can profit from the investigation of stress discovery for psychological wellness expectation, as per earlier examination. In this study, we enhance sentiment and emotion analysis to measure the stress levels of people by analyzing their social networking posts and comments. Using machine learning techniques and the deep learning sentiment analysis model BERT, we analyze sentiment using enormous datasets of tweets. We also used an unsupervised machine learning technique called Latent Dirichlet Allocation to scan a set of papers to find word and phrase patterns and create word groups and related phrases that most accurately represented the documents. This aids in predicting the subject that is connected to the textual information. We will be able to identify users' emotions online with the help of these models. Additionally, stress or depression can be studied using these feelings. In summary, the BERT model and ML models have very high detection rates. The benefits of this research can be shown in terms of mental wellness.