Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling
At the end of 2019, while the world was being hit by the COVID-19 virus and, consequently, was living a global health crisis, many other pandemics were putting humankind in danger. The role of social media is of paramount importance in these kinds of contexts because they help health systems to cope...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/23/11924 |
| _version_ | 1797463680441384960 |
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| author | Zhikang Qin Elisabetta Ronchieri |
| author_facet | Zhikang Qin Elisabetta Ronchieri |
| author_sort | Zhikang Qin |
| collection | DOAJ |
| description | At the end of 2019, while the world was being hit by the COVID-19 virus and, consequently, was living a global health crisis, many other pandemics were putting humankind in danger. The role of social media is of paramount importance in these kinds of contexts because they help health systems to cope with emergencies by contributing to conducting some activities, such as the identification of public concerns, the detection of infections’ symptoms, and the traceability of the virus diffusion. In this paper, we have analysed comments on events related to cholera, Ebola, HIV/AIDS, influenza, malaria, Spanish influenza, swine flu, tuberculosis, typhus, yellow fever, and Zika, collecting 369,472 tweets from 3 March to 15 September 2022. Our analysis has started with the collection of comments composed of unstructured texts on which we have applied natural language processing solutions. Following, we have employed topic modelling and sentiment analysis techniques to obtain a collection of people’s concerns and attitudes towards these pandemics. According to our findings, people’s discussions were mostly about malaria, influenza, and tuberculosis, and the focus was on the diseases themselves. As regards emotions, the most popular were fear, trust, and disgust, where trust is mainly regarding HIV/AIDS tweets. |
| first_indexed | 2024-03-09T17:54:11Z |
| format | Article |
| id | doaj.art-600dad312d044c9cbf61f29c1f793d9b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:54:11Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-600dad312d044c9cbf61f29c1f793d9b2023-11-24T10:28:04ZengMDPI AGApplied Sciences2076-34172022-11-0112231192410.3390/app122311924Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic ModellingZhikang Qin0Elisabetta Ronchieri1Department of Statistical Sciences, University of Bologna, 40126 Bologna, ItalyDepartment of Statistical Sciences, University of Bologna, 40126 Bologna, ItalyAt the end of 2019, while the world was being hit by the COVID-19 virus and, consequently, was living a global health crisis, many other pandemics were putting humankind in danger. The role of social media is of paramount importance in these kinds of contexts because they help health systems to cope with emergencies by contributing to conducting some activities, such as the identification of public concerns, the detection of infections’ symptoms, and the traceability of the virus diffusion. In this paper, we have analysed comments on events related to cholera, Ebola, HIV/AIDS, influenza, malaria, Spanish influenza, swine flu, tuberculosis, typhus, yellow fever, and Zika, collecting 369,472 tweets from 3 March to 15 September 2022. Our analysis has started with the collection of comments composed of unstructured texts on which we have applied natural language processing solutions. Following, we have employed topic modelling and sentiment analysis techniques to obtain a collection of people’s concerns and attitudes towards these pandemics. According to our findings, people’s discussions were mostly about malaria, influenza, and tuberculosis, and the focus was on the diseases themselves. As regards emotions, the most popular were fear, trust, and disgust, where trust is mainly regarding HIV/AIDS tweets.https://www.mdpi.com/2076-3417/12/23/11924epidemicsTwitternatural language processingtopic modellingsentiment analysisARI |
| spellingShingle | Zhikang Qin Elisabetta Ronchieri Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling Applied Sciences epidemics natural language processing topic modelling sentiment analysis ARI |
| title | Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling |
| title_full | Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling |
| title_fullStr | Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling |
| title_full_unstemmed | Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling |
| title_short | Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling |
| title_sort | exploring pandemics events on twitter by using sentiment analysis and topic modelling |
| topic | epidemics natural language processing topic modelling sentiment analysis ARI |
| url | https://www.mdpi.com/2076-3417/12/23/11924 |
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