Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions
The interest in social media sentiment analysis and opinion mining for public security events has increased over the years. The availability of social media platforms for communication provides a valuable source of information for sentiment analysis and opinion mining research. The content shared ac...
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2023
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Online Access: | http://ir.unimas.my/id/eprint/42904/1/Social%20Media.pdf http://ir.unimas.my/id/eprint/42904/ https://www.sciencedirect.com/science/article/pii/S1319157823003300 https://doi.org/10.1016/j.jksuci.2023.101776 |
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my.unimas.ir.429042023-10-02T07:08:24Z http://ir.unimas.my/id/eprint/42904/ Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions Mohd Suhairi, Md Suhaimin Mohd Hanafi, Ahmad Hijazi Ervin Gubin, Moung Puteri Nor Ellyza, Nohuddin Stephanie, Chua Frans, Coenen QA75 Electronic computers. Computer science The interest in social media sentiment analysis and opinion mining for public security events has increased over the years. The availability of social media platforms for communication provides a valuable source of information for sentiment analysis and opinion mining research. The content shared across the media gives potential input to the physical environment and social phenomena related to public security threats. The input has been used to: monitor public security threats or emergency events, analyzing sentiment and opinionated data for threat management and the detection of public security threat events using geographic location-based sentiment analysis. However, a systematic survey that describes the trends and latest developments in this domain is unavailable. This paper presents a survey of social media sentiment analysis and opinion mining for public security. This paper aims to: understand the progress of the current state-of-the-art, identify the research gaps, and propose potential future directions. In total, 200 articles published from 2016 to 2023 were considered in this survey. The taxonomy shows the key attributes and limitations of the work presented in the surveyed articles. Subsequently, the potential future direction of work on sentiment analysis in the public security domain is suggested for interested researchers. Elsevier B.V. 2023-09-26 Article PeerReviewed text en http://ir.unimas.my/id/eprint/42904/1/Social%20Media.pdf Mohd Suhairi, Md Suhaimin and Mohd Hanafi, Ahmad Hijazi and Ervin Gubin, Moung and Puteri Nor Ellyza, Nohuddin and Stephanie, Chua and Frans, Coenen (2023) Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions. Journal of King Saud University - Computer and Information Sciences (101776). pp. 1-64. ISSN 1319-1578 https://www.sciencedirect.com/science/article/pii/S1319157823003300 https://doi.org/10.1016/j.jksuci.2023.101776 |
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QA75 Electronic computers. Computer science Mohd Suhairi, Md Suhaimin Mohd Hanafi, Ahmad Hijazi Ervin Gubin, Moung Puteri Nor Ellyza, Nohuddin Stephanie, Chua Frans, Coenen Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions |
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The interest in social media sentiment analysis and opinion mining for public security events has increased over the years. The availability of social media platforms for communication provides a valuable source of information for sentiment analysis and opinion mining research. The content shared across the media gives potential input to the physical environment and social phenomena related to public security threats. The input has been used to: monitor public security threats or emergency events, analyzing sentiment and opinionated data for threat management and the detection of public security threat events using geographic location-based sentiment analysis. However, a systematic survey that describes the trends and latest developments in this domain is unavailable. This paper presents a survey of social media sentiment analysis and opinion mining for public security. This paper aims to: understand the progress of the current state-of-the-art, identify the research gaps, and propose potential future directions. In total, 200 articles published from 2016 to 2023 were considered in this survey. The taxonomy shows the key attributes and limitations of the work presented in the surveyed articles. Subsequently, the potential future direction of work on sentiment analysis in the public security domain is suggested for interested researchers. |
format |
Article |
author |
Mohd Suhairi, Md Suhaimin Mohd Hanafi, Ahmad Hijazi Ervin Gubin, Moung Puteri Nor Ellyza, Nohuddin Stephanie, Chua Frans, Coenen |
author_facet |
Mohd Suhairi, Md Suhaimin Mohd Hanafi, Ahmad Hijazi Ervin Gubin, Moung Puteri Nor Ellyza, Nohuddin Stephanie, Chua Frans, Coenen |
author_sort |
Mohd Suhairi, Md Suhaimin |
title |
Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions |
title_short |
Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions |
title_full |
Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions |
title_fullStr |
Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions |
title_full_unstemmed |
Social Media Sentiment Analysis and Opinion Mining in Public Security : Taxonomy, Trend Analysis, Issues and Future Directions |
title_sort |
social media sentiment analysis and opinion mining in public security : taxonomy, trend analysis, issues and future directions |
publisher |
Elsevier B.V. |
publishDate |
2023 |
url |
http://ir.unimas.my/id/eprint/42904/1/Social%20Media.pdf http://ir.unimas.my/id/eprint/42904/ https://www.sciencedirect.com/science/article/pii/S1319157823003300 https://doi.org/10.1016/j.jksuci.2023.101776 |
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1779150694558728192 |