Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning
5th International Conference on Communication, Device and Networking, ICCDN 2021
; 902:401-412, 2023.
Article
in English
| Scopus | ID: covidwho-2048170
ABSTRACT
The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Bidirectional long short-term memory (Bi-LSTM); Convolutional neural networks (CNNs); Deep learning; Long short-term memory (LSTM); Post-COVID-19; Predictive analysis; Recurrent neural network (RNN); Brain; Convolutional neural networks; Digital storage; Learning systems; Long short-term memory; Predictive analytics; Viruses; Bidirectional long short-term memory; Convolutional neural network; Emotion predictions; Human being; Human emotion; Recurrent neural network; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Topics:
Long Covid
Language:
English
Journal:
5th International Conference on Communication, Device and Networking, ICCDN 2021
Year:
2023
Document Type:
Article
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