Depression detection from Twitter posts using NLP and Machine learning techniques

Depression is the one of the most seviour mental issue that the people of world-wide are irrelevant of their ages gender caste and races‥etc. In this modern communication world peoples are more comport to express their thoughts in front of social media almost every day. The main agenda of this paper is to propose the data-analytics based model to detect depressed tweeter tweets of the peoples. In this paper then data is going to collect from different user's posted tweets from most popular social-media website like twitter. The depression level can be identified based on the tweets of the users in social-media. The standard methods to detect depression of the users via tweets which is in the form of structured, these methods needs a larger amount of the data from the users. Now a day's social media platform like twitter. Twitter has become more popular to express their views and their emotions in the form of tweets. The data screening can be done based on tweets it shows depressive symptoms of the users. By using machine learning technique we are going to do pre-processing of the data collected from the users. And even using Recurrent neural network (RNN) and NLP techniques, LSTM Deep-learning techniques to identify the depressed tweets in a more convenient manner.