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Depression Discovery in Covid-19 Communities using Deep Learning


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


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.


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


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

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