Article ; Online: An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets.
Mathematical biosciences and engineering : MBE
2022 Volume 20, Issue 2, Page(s) 2382–2407
Abstract: The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-19. ... ...
| Abstract | The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-19. The rise in the number of COVID-19 cases and deaths across the world has caused fear, anxiety and depression among individuals. Social media is the most dominant tool that disturbed human life during this pandemic. Among the social media platforms, Twitter is one of the most prominent and trusted social media platforms. To control and monitor the COVID-19 infection, it is necessary to analyze the sentiments of people expressed on their social media platforms. In this study, we proposed a deep learning approach known as a long short-term memory (LSTM) model for the analysis of tweets related to COVID-19 as positive or negative sentiments. In addition, the proposed approach makes use of the firefly algorithm to enhance the overall performance of the model. Further, the performance of the proposed model, along with other state-of-the-art ensemble and machine learning models, has been evaluated by using performance metrics such as accuracy, precision, recall, the AUC-ROC and the F1-score. The experimental results reveal that the proposed LSTM + Firefly approach obtained a better accuracy of 99.59% when compared with the other state-of-the-art models. |
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| MeSH term(s) | Humans ; Sentiment Analysis ; COVID-19 ; Deep Learning ; Algorithms ; Fear |
| Language | English |
| Publishing date | 2022-11-21 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2265126-3 |
| ISSN | 1551-0018 ; 1551-0018 |
| ISSN (online) | 1551-0018 |
| ISSN | 1551-0018 |
| DOI | 10.3934/mbe.2023112 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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