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Prediction of Mental Health among Twitter users

Suvarna, Naval (2020) Prediction of Mental Health among Twitter users. Masters thesis, Dublin, National College of Ireland.

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Abstract

Awareness about Mental health has been on the rise in the past few years as opposed to the earlier ages when it was neglected due to lack of physical evidence and the fear of being stigmatized. Along with one’s physical health, mental health is also crucial to an individual’s well-being. Sometimes this mental health can be affected due to life-style changes or circumstances that induce a life-altering feeling. When mental health is compromised it could lead to the deterioration of an individual’s overall-health. Signs of mental distress can often be found on the online activities of individuals via exploration of posts that have been uploaded online. Since traditional diagnosis take place after the condition has turned worse, there is a need for a quick yet accurate system which would provide early warnings about the condition of such individuals. This research has been carried out to predict mental health of Twitter users using machine learning models and deep learning and also to recognize the optimum model that could be used on such data. The research culminates by recognizing that MLP classifier and Random forest classifier are the most optimum models that can be used to predict the mental health of individuals using tweets.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

R Medicine > RA Public aspects of medicine > RA790 Mental Health
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 21 Jan 2021 11:21
Last Modified: 21 Jan 2021 11:21
URI: http://norma.ncirl.ie/id/eprint/4423

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