Oriental Journal of Computer Science and Technology

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DEPRESSIKA: An Early Risk of Depression Detection through Opinions


Affiliations
1 Department of IT, LBEF (APUTI), Kathmandu, Nepal
 

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.

Keywords

Depression Detection, Deep Learning [DL], Machine Learning [ML], Recurrent Neural Networks [RNN], Sequential, Sentiment Classification.
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  • DEPRESSIKA: An Early Risk of Depression Detection through Opinions

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Authors

Abhusan Chataut
Department of IT, LBEF (APUTI), Kathmandu, Nepal
Jyotir Moy Chatterjee
Department of IT, LBEF (APUTI), Kathmandu, Nepal
Rabi Shankar Rouniyar
Department of IT, LBEF (APUTI), Kathmandu, Nepal

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.

Keywords


Depression Detection, Deep Learning [DL], Machine Learning [ML], Recurrent Neural Networks [RNN], Sequential, Sentiment Classification.

References