{"title":"RepComment:一个公平的评论情感表达系统","authors":"Ting Wu, Chunxi Tan, Ming Cheung, P. Hui","doi":"10.1109/ICDMW.2014.22","DOIUrl":null,"url":null,"abstract":"Most Online Social Networks (OSNs) allow registered members to leave comments on particular entities. An entity can either be a person, a location, or a product. These comments have already become an important reference for many people in the daily life. However, a popular entity usually receives an extensive number of comments and it has become infeasible for users to read through all of them. In this demonstration, we propose Rep Comment, a fair comment-sentiment representation system based on a novel probability sampling model that can choose a small set of comments (samples) that are most resemble and representative for the original comment set. The proposed approximation algorithm significantly reduces the computation cost of the sampling problem while keeping relatively high accuracy.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RepComment: A Fair Comment-Sentiment Representation System\",\"authors\":\"Ting Wu, Chunxi Tan, Ming Cheung, P. Hui\",\"doi\":\"10.1109/ICDMW.2014.22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most Online Social Networks (OSNs) allow registered members to leave comments on particular entities. An entity can either be a person, a location, or a product. These comments have already become an important reference for many people in the daily life. However, a popular entity usually receives an extensive number of comments and it has become infeasible for users to read through all of them. In this demonstration, we propose Rep Comment, a fair comment-sentiment representation system based on a novel probability sampling model that can choose a small set of comments (samples) that are most resemble and representative for the original comment set. The proposed approximation algorithm significantly reduces the computation cost of the sampling problem while keeping relatively high accuracy.\",\"PeriodicalId\":289269,\"journal\":{\"name\":\"2014 IEEE International Conference on Data Mining Workshop\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Data Mining Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2014.22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Data Mining Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2014.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RepComment: A Fair Comment-Sentiment Representation System
Most Online Social Networks (OSNs) allow registered members to leave comments on particular entities. An entity can either be a person, a location, or a product. These comments have already become an important reference for many people in the daily life. However, a popular entity usually receives an extensive number of comments and it has become infeasible for users to read through all of them. In this demonstration, we propose Rep Comment, a fair comment-sentiment representation system based on a novel probability sampling model that can choose a small set of comments (samples) that are most resemble and representative for the original comment set. The proposed approximation algorithm significantly reduces the computation cost of the sampling problem while keeping relatively high accuracy.
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