Novel Deep Neural Network Based Stress Detection System
COVID 19; LSTM; RMSProp; RNN; Sigmoid; Stress
Stress is a state of tension on an emotional or bodily level. Frustration, despair, anxiety, and other mental health problems can all be brought on by Stress. Strain is a side effect of Stress. People can openly share their views and opinions on social media networking sites like Twitter and Facebook, which are highly popular. The COVID 19 pandemic has wreaked havoc on millions of peoples lives all across the world. The public has experienced Stress as a result of the various measures employed to stop the spread of COVID 19, including confinement and social isolation. The current research seeks to develop an unique COVID 19 scenario-based deep neural network-based Stress detection system using tweets related to COVID 19. We use deep learning to create three models. RNN with single LSTM layer, two layers of LSTM with RNN followed by bidirectional LSTM layer is built to detect Stress for the considered dataset. A number of recurrent neural networks are built upon the Keras layers. The optimization algorithm called RMSProp and Sigmoid activation function is used. It is observed that RNN with 2 layers of LSTM outperforms the other deep learning architectures constructed. 2023 American Institute of Physics Inc.. All rights reserved.
AIP Conference Proceedings, Vol-2909, No. 1
https://doi.org/10.1063/5.0181900 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179877706&doi=10.1063%2f5.0181900&partnerID=40&md5=69057dd796dafa5e74181df0e34665f2
Jyothsna R., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Rohini V., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Paulose J., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India