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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.

Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.

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