Advanced Analytical Models for Analyzing Financial and Social Data
NLP , Machine Learning , Depression Detection , Social Support Group , Bankruptcy Prediction
Depression is a common mental disorder affecting thoughts, feelings, behaviors, or moods. We develop a methodological language framework that incorporates psychological patterns and states, contextual information, and the influence of social interactions using NLP and ML techniques to detect user-level depression more effectively and accurately from social networks. Social support via social networks can affect mental health outcomes by promoting positive mental states, providing socialization opportunities, and supportive relationships. We define a new group search problem over social networks to find support groups for depressed users. We prove this problem is NP-hard and propose a 2-approximation algorithm. We also conduct experiments using real-world datasets to show that our greedy algorithm can efficiently find groups with shared interests and small proximity. We also use machine learning to enhance business decisions using financial data. We propose a new sampling method specifically suited to the bankruptcy prediction problem. We then calibrate a random forest model as an easy-to-interpret and easy-to-visualize analytic tool to support better investment decision-making by identifying companies at risk of default.