Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Social media content is mostly used for review, opinion, influence, or sentiment analysis. In this paper, we aim to extend sentiment and emotion analysis for detecting the stress of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to accomplish sentiment analysis with the aid of machine learning algorithms and a deep learning model, BERT for sentiment classification. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method for scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us to predict which topic is linked to the textual data. With the aid of these models, we will be able to detect the emotion of users online. Further, these emotions can be used to analyze stress or depression. In conclusion, the ML models and a BERT model have a very good detection rate. This research is useful for the well-being of one's mental health. The results are evaluated using various metrics at the macro and micro levels and indicate that the trained model detects the status of emotions based on social interactions.

Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Subsequently, it can be used as a very effective tool in detecting the stress levels of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to successfully accomplish sentiment analysis with the aid of machine learning algorithms. We take the help of a capable deep learning pre-trained model called BERT to solve the problems which come with sentiment classification. The BERT model outperforms a lot of other well-known models for this job without any sophisticated architecture. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method that’s skilled in scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us predict which topic is linked to the textual data. With the aid of the models suggested, we will be able to detect the emotion of users online. We are primarily working with Twitter data because Twitter is a website where people express their thoughts often. In conclusion, this proposal is for the well- being of one’s mental health. The results are evaluated using various metric at macro and micro level and indicate that the trained model detects the status of emotions bases on social interactions.