{"title":"从意见网络中寻找领导者","authors":"Hengmin Zhou, D. Zeng, Changli Zhang","doi":"10.1109/ISI.2009.5137323","DOIUrl":null,"url":null,"abstract":"This paper is motivated to utilize results from opinion mining to facilitate social network analysis. We introduce the concept of Opinion Networks and propose a PageRank-like algorithm, named OpinionRank, to rank the nodes in an opinion network. This proposed approach has been applied to real-world datasets and initial experiments indicate that the sentiment information is helpful for finding leaders of online communities and that the OpinionRank method outperforms benchmark methods that ignore sentiment information.","PeriodicalId":210911,"journal":{"name":"2009 IEEE International Conference on Intelligence and Security Informatics","volume":"64 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"49","resultStr":"{\"title\":\"Finding leaders from opinion networks\",\"authors\":\"Hengmin Zhou, D. Zeng, Changli Zhang\",\"doi\":\"10.1109/ISI.2009.5137323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is motivated to utilize results from opinion mining to facilitate social network analysis. We introduce the concept of Opinion Networks and propose a PageRank-like algorithm, named OpinionRank, to rank the nodes in an opinion network. This proposed approach has been applied to real-world datasets and initial experiments indicate that the sentiment information is helpful for finding leaders of online communities and that the OpinionRank method outperforms benchmark methods that ignore sentiment information.\",\"PeriodicalId\":210911,\"journal\":{\"name\":\"2009 IEEE International Conference on Intelligence and Security Informatics\",\"volume\":\"64 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"49\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Intelligence and Security Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISI.2009.5137323\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Intelligence and Security Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISI.2009.5137323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper is motivated to utilize results from opinion mining to facilitate social network analysis. We introduce the concept of Opinion Networks and propose a PageRank-like algorithm, named OpinionRank, to rank the nodes in an opinion network. This proposed approach has been applied to real-world datasets and initial experiments indicate that the sentiment information is helpful for finding leaders of online communities and that the OpinionRank method outperforms benchmark methods that ignore sentiment information.