Advanced Analytical Models for Analyzing Financial and Social Data
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Authors
Yang, Xingwei
Date
Type
thesis
Language
eng
Keyword
NLP , Machine Learning , Depression Detection , Social Support Group , Bankruptcy Prediction
Alternative Title
Abstract
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.
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License
Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.