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Depression Discovery in Covid-19 Communities using Deep Learning

Author(s):

Sourabh Sharma, Saloni Yadav and Vaishali Kalra*   Pages 1 - 12 ( 12 )

Abstract:


Seemingly, in the contemporary era, data is the new currency. Facing the exponential explosion of data through its various online sources, there arises a need for the industry to tap into this source. The art of ascribing sentiment to a piece of text is Sentiment Analysis making it relevant to a wide array of fields. The world has witnessed assortments of a wide family of coronaviruses, 229E and NL63 being the alpha coronaviruses and OC43 & HKU1 being the beta coronaviruses. A version of coronavirus MERS-CoV seemed to be identified in Saudi Arabia. This work focuses on The Deep Neural Network and Natural Language processing to detect depression in COVID-19 patients, and the model is trained with the depression labeled tagged dataset with an accuracy of 98%. The foundation of this work shall be instrumental in aiding the doctors for the timely diagnosis and treatment of their patients.

Keywords:

Covid-19, LSTM, convolutional neural networks, depression detection, word embeddings, natural language processing, machine learning algorithm.

Affiliation:

Department of CSE, The NorthCap University, Department of CSE, The NorthCap University, Department of CSE, The NorthCap University



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