PREDICTING DEPRESSION LEVEL USING SOCIAL MEDIA POSTS
Keywords:Naive Bayes Algorithm, Sentiment Analysis, Social Networking Websites, Text Mining, User Generated Data
Depression is a major concern snowballing day by day. There can be various causes of depression but mental illness is the main problem. A lot of people suffer from depression and a very few of them go through treatment. One out of six people between ages 10 to 19 years are suffering from depression. At its worst, depression can lead to suicide. Depression reduces user’s ability to do work study or socialize. One solution to this problem is study of individual’s behaviour through social media. We could know a person’s opinion, thinking, mood etc. through his social media. These attributes of user can be collected from different social networking sites like Instagram, Facebook, and Twitter etc. Social networking sites can be used as an analysis tool to predict depression level. Our projects aim is to gather information of user from their social media posts and predict his depression level.
Munmun De Choudhury, Michael Gamon, Scott by Counts, Eric Horvitz Predicting Depression via Social Media 2013
Christos Troussas, Maria Virvou,Kurt Junshean Espinosa, Kevin Llaguno, Jaime Caro Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. 2013 DOI: https://doi.org/10.1109/IISA.2013.6623713
Alec Go,Richa Bhayani,Lei Huang Twitter sentiment classification using distant supervision 2009
Namrata Sonawane, Mayuri Padmane, Vishwja Suralkar, Snehal Wable, Prakash Date via Predicting Depression Level Using Social Media Posts.
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