{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T12:22:22Z","timestamp":1711455742082},"reference-count":58,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&DProgram of China","award":["Grant 2017YFB0802805"]},{"name":"2020 Industrial Internet Innovation and Development Project"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2021,10]]},"abstract":"\u201cSocial sensors\u201d refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the \u201cCOVID-19\u201d collected from 1 May 2020 to 9 July 2020. We build users\u2019 portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users\u2019 portraits. We analyze the influence of users\u2019 features on the sentiment. The prediction accuracy of our model is 64.88%.<\/jats:p>","DOI":"10.1177\/15501477211033765","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T05:44:22Z","timestamp":1635486262000},"page":"155014772110337","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors"],"prefix":"10.1177","volume":"17","author":[{"given":"Bin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5953-5120","authenticated-orcid":false,"given":"Enhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing, China"},{"name":"College of Computer and 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