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Detecting Depression in Tweets

Shailesh Sachan, Mujtaba Shamsuddin Sayyed, Taha Khan, Diksha G Kumar

Abstract


Depression is a serious mental health issue for people worldwide irrelevant of their ages, genders and races. In this age of recent communication and technology, people feel easier to share their thoughts on social networking sites (SNS) almost a day. The goal is to propose an information logical based model to recognize despondency of people. The proposed model data is collected from the users' posts of two popular social media websites: Twitter and Facebook. The Depression level of a user has been detected supported his posts on social media. The basic method of detecting depression in any person is the structured or a semi-structured interview method. These techniques need a tremendous measure of information from the individual. Blogging sites such as Twitter and Facebook have become so many popular places to express peoples activity and thoughts. The data screening from tweets and posts shows the manifestation of depressive disorder symptoms of the user. ML (machine learning)is used to process the scrapped data collected from SNS users. Natural Language Processing classified using Support Vector Machine and Naïve Bayes algorithm to detect depression potentially in a more convenient and efficient way


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References


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