Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks
In the past two decades, the growth of social data on the web has rapidly increased. This leads to researchers to access the data and information for many academic research and commercial uses. Social data on the web contains many real life events that occurred in daily life, today the global COVID-...
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| Format: | Article |
| Language: | English |
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Sulaimani Polytechnic University
2020-05-01
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| Series: | Kurdistan Journal of Applied Research |
| Subjects: | |
| Online Access: | https://kjar.spu.edu.iq/index.php/kjar/article/view/512 |
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| author | Kamaran H. Manguri Rebaz N. Ramadhan Pshko R. Mohammed Amin |
| author_facet | Kamaran H. Manguri Rebaz N. Ramadhan Pshko R. Mohammed Amin |
| author_sort | Kamaran H. Manguri |
| collection | DOAJ |
| description | In the past two decades, the growth of social data on the web has rapidly increased. This leads to researchers to access the data and information for many academic research and commercial uses. Social data on the web contains many real life events that occurred in daily life, today the global COVID-19 disease is spread worldwide. Many individuals including media organizations and government agencies are presenting the latest news and opinions regarding the coronavirus. In this study, the twitter data has been pulled out from Twitter social media, through python programming language, using Tweepy library, then by using TextBlob library in python, the sentiment analysis operation has been done. After the measuring sentiment analysis, the graphical representation has been provided on the data. The data we have collected on twitter are based on two specified hashtag keywords, which are (“COVID-19, coronavirus”). The date of searching data is seven days from 09-04-2020 to 15-04-2020. In the end a visualized presentation regarding the results and further explanation are provided.
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| format | Article |
| id | doaj-art-69bec09eb87b4b6fa105a4c1dff53b0a |
| institution | Kabale University |
| issn | 2411-7684 2411-7706 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Sulaimani Polytechnic University |
| record_format | Article |
| series | Kurdistan Journal of Applied Research |
| spelling | doaj-art-69bec09eb87b4b6fa105a4c1dff53b0a2025-02-09T21:00:17ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062020-05-015310.24017/covid.8Twitter Sentiment Analysis on Worldwide COVID-19 OutbreaksKamaran H. Manguri0Rebaz N. Ramadhan1Pshko R. Mohammed Amin2Department of Computer Science, College of Basic Education, University of Raparin, Rania, IraqSoftware Engineering Department, Faculty of Engineering, Koya University, Koya, IraqDepartment of Computer Science, College of Basic Education, University of Raparin, Rania, IraqIn the past two decades, the growth of social data on the web has rapidly increased. This leads to researchers to access the data and information for many academic research and commercial uses. Social data on the web contains many real life events that occurred in daily life, today the global COVID-19 disease is spread worldwide. Many individuals including media organizations and government agencies are presenting the latest news and opinions regarding the coronavirus. In this study, the twitter data has been pulled out from Twitter social media, through python programming language, using Tweepy library, then by using TextBlob library in python, the sentiment analysis operation has been done. After the measuring sentiment analysis, the graphical representation has been provided on the data. The data we have collected on twitter are based on two specified hashtag keywords, which are (“COVID-19, coronavirus”). The date of searching data is seven days from 09-04-2020 to 15-04-2020. In the end a visualized presentation regarding the results and further explanation are provided. https://kjar.spu.edu.iq/index.php/kjar/article/view/512Sentiment analysis, COVID-19, Coronavirus, Social Media, Twitter, Python, Text Blob. |
| spellingShingle | Kamaran H. Manguri Rebaz N. Ramadhan Pshko R. Mohammed Amin Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks Kurdistan Journal of Applied Research Sentiment analysis, COVID-19, Coronavirus, Social Media, Twitter, Python, Text Blob. |
| title | Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks |
| title_full | Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks |
| title_fullStr | Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks |
| title_full_unstemmed | Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks |
| title_short | Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks |
| title_sort | twitter sentiment analysis on worldwide covid 19 outbreaks |
| topic | Sentiment analysis, COVID-19, Coronavirus, Social Media, Twitter, Python, Text Blob. |
| url | https://kjar.spu.edu.iq/index.php/kjar/article/view/512 |
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