Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City
With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinion...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9789431 |
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| _version_ | 1849692568397283328 |
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| author | Yanni Liu Dongsheng Liu Yuwei Chen |
| author_facet | Yanni Liu Dongsheng Liu Yuwei Chen |
| author_sort | Yanni Liu |
| collection | DOAJ |
| description | With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained. |
| format | Article |
| id | doaj-art-ea59ea043b7a47fb84addff8c20af4da |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ea59ea043b7a47fb84addff8c20af4da2025-08-20T03:20:39ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/97894319789431Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart CityYanni Liu0Dongsheng Liu1Yuwei Chen2School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, ChinaBeijing Yunzhenxin Technology Co., Ltd., Hangzhou 310012, ChinaWith the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.http://dx.doi.org/10.1155/2020/9789431 |
| spellingShingle | Yanni Liu Dongsheng Liu Yuwei Chen Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City Complexity |
| title | Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City |
| title_full | Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City |
| title_fullStr | Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City |
| title_full_unstemmed | Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City |
| title_short | Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City |
| title_sort | research on sentiment tendency and evolution of public opinions in social networks of smart city |
| url | http://dx.doi.org/10.1155/2020/9789431 |
| work_keys_str_mv | AT yanniliu researchonsentimenttendencyandevolutionofpublicopinionsinsocialnetworksofsmartcity AT dongshengliu researchonsentimenttendencyandevolutionofpublicopinionsinsocialnetworksofsmartcity AT yuweichen researchonsentimenttendencyandevolutionofpublicopinionsinsocialnetworksofsmartcity |