An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets
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 de...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023112?viewType=HTML |
| _version_ | 1811175562443489280 |
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| author | H. Swapnarekha Janmenjoy Nayak H. S. Behera Pandit Byomakesha Dash Danilo Pelusi |
| author_facet | H. Swapnarekha Janmenjoy Nayak H. S. Behera Pandit Byomakesha Dash Danilo Pelusi |
| author_sort | H. Swapnarekha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T19:37:48Z |
| format | Article |
| id | doaj.art-8feeecddef604f58ad28dc91431a8ab8 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T19:37:48Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-8feeecddef604f58ad28dc91431a8ab82023-01-30T01:11:55ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-01-012022382240710.3934/mbe.2023112An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweetsH. Swapnarekha0Janmenjoy Nayak1H. S. Behera 2Pandit Byomakesha Dash3Danilo Pelusi41. Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India3. Department of Information Technology, Veer Surendra Sai University of Technology, Burla 768018, India2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha 757003, India3. Department of Information Technology, Veer Surendra Sai University of Technology, Burla 768018, India1. Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India4. Communication Sciences, University of Teramo, Coste Sant'agostino Campus, Teramo 64100, ItalyThe 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.https://www.aimspress.com/article/doi/10.3934/mbe.2023112?viewType=HTMLlong short-term memorycovid-19sentiment analysistweetsfirefly algorithm |
| spellingShingle | H. Swapnarekha Janmenjoy Nayak H. S. Behera Pandit Byomakesha Dash Danilo Pelusi An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets Mathematical Biosciences and Engineering long short-term memory covid-19 sentiment analysis tweets firefly algorithm |
| title | An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets |
| title_full | An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets |
| title_fullStr | An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets |
| title_full_unstemmed | An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets |
| title_short | An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets |
| title_sort | optimistic firefly algorithm based deep learning approach for sentiment analysis of covid 19 tweets |
| topic | long short-term memory covid-19 sentiment analysis tweets firefly algorithm |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023112?viewType=HTML |
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