Social media sentiment analysis based on COVID-19
In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, pos...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
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| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24751839.2020.1790793 |
| _version_ | 1818672180815200256 |
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| author | László Nemes Attila Kiss |
| author_facet | László Nemes Attila Kiss |
| author_sort | László Nemes |
| collection | DOAJ |
| description | In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media platform, based on the main trends (by keyword, which is mostly the ‘covid’ and coronavirus theme in this article) with Natural Language Processing and with Sentiment Classification using Recurrent Neural Network. Where we analyse, compile, visualize statistics, and summarize for further processing. The trained model works much more accurately, with a smaller margin of error, in determining emotional polarity in today's ‘modern’ often with ambiguous tweets. Especially with RNN. We use this fresh scraped data collections (by the keyword's theme) with our RNN model what we have created and trained to determine what emotional manifestations occurred on a given topic in a given time interval. |
| first_indexed | 2024-12-17T07:35:48Z |
| format | Article |
| id | doaj.art-13cf8516b0da4bfc9b9654f74de931f7 |
| institution | Directory Open Access Journal |
| issn | 2475-1839 2475-1847 |
| language | English |
| last_indexed | 2024-12-17T07:35:48Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj.art-13cf8516b0da4bfc9b9654f74de931f72022-12-21T21:58:20ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472021-01-015111510.1080/24751839.2020.17907931790793Social media sentiment analysis based on COVID-19László Nemes0Attila Kiss1Department of Information Systems, ELTE Eötvös Loránd UniversityDepartment of Information Systems, ELTE Eötvös Loránd UniversityIn today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media platform, based on the main trends (by keyword, which is mostly the ‘covid’ and coronavirus theme in this article) with Natural Language Processing and with Sentiment Classification using Recurrent Neural Network. Where we analyse, compile, visualize statistics, and summarize for further processing. The trained model works much more accurately, with a smaller margin of error, in determining emotional polarity in today's ‘modern’ often with ambiguous tweets. Especially with RNN. We use this fresh scraped data collections (by the keyword's theme) with our RNN model what we have created and trained to determine what emotional manifestations occurred on a given topic in a given time interval.http://dx.doi.org/10.1080/24751839.2020.1790793natural language processingrecurrent neural networksentiment analysissocial mediavisualization |
| spellingShingle | László Nemes Attila Kiss Social media sentiment analysis based on COVID-19 Journal of Information and Telecommunication natural language processing recurrent neural network sentiment analysis social media visualization |
| title | Social media sentiment analysis based on COVID-19 |
| title_full | Social media sentiment analysis based on COVID-19 |
| title_fullStr | Social media sentiment analysis based on COVID-19 |
| title_full_unstemmed | Social media sentiment analysis based on COVID-19 |
| title_short | Social media sentiment analysis based on COVID-19 |
| title_sort | social media sentiment analysis based on covid 19 |
| topic | natural language processing recurrent neural network sentiment analysis social media visualization |
| url | http://dx.doi.org/10.1080/24751839.2020.1790793 |
| work_keys_str_mv | AT laszlonemes socialmediasentimentanalysisbasedoncovid19 AT attilakiss socialmediasentimentanalysisbasedoncovid19 |