DEPRESSIKA: An Early Risk of Depression Detection through Opinions

Abstract

Deep learning is a very dynamic area in Sentiment Classification. Text analytics is the process of understanding text and making actionable decisions and acting on it. be it Amazon Alexa, Siri, Cortana everything is made up of Natural Language Processing. Text to speech and Speech to text are generating so many data sets every day. The internet has the largest repository of data, it is hard to define what to exactly do with it. sentiment are the opinions or the way of feelings of the public usually in the sequential form, in which many people face difficulty in living their daily life. Some are even ending their life just they are depressed. The approach here is to help the people suffering from depression with appropriate methodology to use in this work. Depressika: Early Risk of Depression Detection with opinions is a web application which detects the early risk of depression from the social media posts created by the users with appropriate Recurrent Neural Networks [RNN]. This is a classification problem of the Machine Learning [ML]. Depressika builds on Waterfall Methodology of application development using the Keras, Tensor Flow, Scikit-Learn and Matplotlib to carryout and process sequential data and the overall process of development is carried out by Python programming Language. CONTACT Jyotir Moy Chatterjee jyotirchatterjee@gmail.com Department of IT, LBEF (APUTI), Kathmandu, Nepal. © 2020 The Author(s). Published by Oriental Scientific Publishing Company This is an Open Access article licensed under a Creative Commons license: Attribution 4.0 International (CC-BY). Doi: 10.13005/ojcst13.01.03 Article History Received: 27 January 2020 Accepted: 13 March 2020