Using Data Analysis and Machine Learning for Studying and Predicting Depression in Users on Social Media
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Mental health problems leading to depression have become a critical concern due to the towering engagement of people on social media platforms. Several past approaches have been implemented by analyzing the pattern, behaviour, and vocabulary of the posts by users on social networking sites. This research proposed a system to predict users who could have been affected by depression, by introspecting characteristics of users already being affected. A combination of both the tweet-level and the user-level architecture was used to generate a more robust and reliable system where semantic embeddings trained from advanced neural networks were adopted under the tweet-level, whilst for the user-level, an approach using 12 significant features was operated by extensive feature engineering. Further, SVM with Word2Vec and TFIDF under tweet-level yielded an accuracy of 98.14% and recall of 95.63%, whereas the gradient boosting classifier under user-level revealed an accuracy of 95.26% with a recall of 86.75%.
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Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2020
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singh-usingdataanalysisandmachinelearningforstudying.pdf | 2023-05-05 | Public | Download |