• Syeda Afra Maryam
• Mubarak Sadiqa
• Pooja Kumari S
• Raqeeb Ahmed Khan
• Sreekantha.b

In today’s world having a population of 7 Billion, 4 Billion people use Social Media amongst which 300 Million are suffering from depression. Suicide being the second major cause of death among 15-29 years of age group which is often led by depression. Mental health in people is a worrying factor and at times it leads to depression and in few cases people may take drastic steps. The project aim to perform depression analysis on Twitter and Reddit data collected from Social Media by extracting tweets & posts from user’s account, preprocessing data and analyzing it by applying sentiment analysis method which is proposed by utilizing vocabulary and man-made dictionary-rules to calculate the sentiment inclination of a person based on his micro-blog. Thus by using Vader and SIA (Sentiment Intensity Analyzer) we analyze the emotions and intensity of the posts and predict the degree of positive, negative and neutral. Further, applied Naive Bayes classifier consisting of 20 class groups/ target names and 18000 records to predict the group of the post. Thus, our model aims to provide a unified interface to track and analyse mental health.

VADER Tool,Sentiment Intensity Analyzer SIA, Naïve Bayes Classifier

"Predictive Mental Health Analysis for social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.e607-e610, July-2022, Available :http://www.jetir.org/papers/JETIR2207477.pdf

"Predictive Mental Health Analysis for social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppe607-e610, July-2022, Available at : http://www.jetir.org/papers/JETIR2207477.pdf