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Depression analysis on users of social network using machine learning algorithms

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dc.contributor.author Akmal Jahan, M. A. C.
dc.contributor.author Vithusa, B.
dc.date.accessioned 2022-12-08T06:49:30Z
dc.date.available 2022-12-08T06:49:30Z
dc.date.issued 2022-11-15
dc.identifier.citation 11th Annual Science Research Sessions 2022 (ASRS-2022) Proceedings on "“Scientific Engagement for Sustainable Futuristic Innovations”. 15th November 2022. Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai, Sri Lanka. pp. 27. en_US
dc.identifier.isbn 978-624-5736-60-7
dc.identifier.uri http://ir.lib.seu.ac.lk/handle/123456789/6358
dc.description.abstract Depression is a serious conditioned mental disorder that has significant effects on the quality of life of a person. Internet sources state that the number of people suffering from depression is getting increased day by day and it affects teenagers more than adults. Our project in this work is to find the status of a user's posts or comments which show depression mood or not, using different types of machine learning classification algorithms. The dataset is collected from users who share their day-to-day status on social networks. The dataset is preprocessed and tokenized to make it compatible to feed into different types of algorithms such as Naïve Bayes, Random Forest, Linear Regression, and Support Vector Machine. During the process, the accuracy level of each algorithm is compared and the algorithm with the highest accuracy has chosen as suitable to process further prediction. en_US
dc.language.iso en_US en_US
dc.publisher Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai. en_US
dc.subject Depression Detection en_US
dc.subject Machine Learning Algorithms en_US
dc.subject Social Network en_US
dc.title Depression analysis on users of social network using machine learning algorithms en_US
dc.type Article en_US


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