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@article{158903,
        author = {Tejas Vaidya  and Rohan Yeole  and Gitesh Paitwar  and Aniket Watpade  and Dr.Namita Kale},
        title = {Deep Learning-Based Early Depression Detection Using Social Media },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {974-977},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158903},
        abstract = {Depression is a serious mental  health issue for people world-wide irrelevant of their ages, genders and races.In this age of modern communication and technology, people feel more comfortable sharing their thoughts in social networking sites (SNS) almost every day. The objective of this paper is to propose a data-analytic based model to detect depression of any human being. In this proposed model data is collected from the users’ posts of popular social media websites: twitter. Depression level of a user has been detected based on his posts in social media. The standard method of detecting depression of a person is a fully structured or a semi-structured interview method (SDI) [1]. These methods need a huge amount of data from the person. Microblogging sites such as twitter and facebook have become so much popular places to express peoples’ activity and thoughts.The data screening from tweets and posts show the manifestation of depressive disorder symptoms of the user. In this research, machine learning is used to process the scrapped data collected from SNS users.Natural Language Processing (NLP), classified using Deep Learning and Naïve Bayes algorithm to detect depression potentially in a more convenient and efficient way.},
        keywords = {Depression, Mental Health, Social Network Sites(SNS), Data Analysis, Deep Learning(DL),Natural Language Processing (NLP).},
        month = {},
        }