{"title":"一种基于网络评论语言结构的情感极性识别新模型——固定情感词模型","authors":"De-Qiang Fan, Su Zhang, B. Li","doi":"10.1109/YCICT.2010.5713107","DOIUrl":null,"url":null,"abstract":"Emotional states are part of the information that is conveyed in many forms of network reviews. This paper presents a new sentiment polarity recognition model based on linguistic structure of emotion states-fixed sentiment terms model. The proposed method uses three types of specific collocation pattern to construct the recognition algorithm based on fixed sentiment terms. These feature term sets are gradually updated by relevance feedbacks from the users which based on incremental tf-idf model. Comparison is done between the traditional method and fixed sentiment terms model. All tests showed the proposed method gets a higher efficiency and accuracy rate of the emotion classifier.","PeriodicalId":179847,"journal":{"name":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new sentiment polarity recognition model based on linguistic structure of network reviews - Fixed sentiment terms model\",\"authors\":\"De-Qiang Fan, Su Zhang, B. Li\",\"doi\":\"10.1109/YCICT.2010.5713107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Emotional states are part of the information that is conveyed in many forms of network reviews. This paper presents a new sentiment polarity recognition model based on linguistic structure of emotion states-fixed sentiment terms model. The proposed method uses three types of specific collocation pattern to construct the recognition algorithm based on fixed sentiment terms. These feature term sets are gradually updated by relevance feedbacks from the users which based on incremental tf-idf model. Comparison is done between the traditional method and fixed sentiment terms model. All tests showed the proposed method gets a higher efficiency and accuracy rate of the emotion classifier.\",\"PeriodicalId\":179847,\"journal\":{\"name\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YCICT.2010.5713107\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YCICT.2010.5713107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new sentiment polarity recognition model based on linguistic structure of network reviews - Fixed sentiment terms model
Emotional states are part of the information that is conveyed in many forms of network reviews. This paper presents a new sentiment polarity recognition model based on linguistic structure of emotion states-fixed sentiment terms model. The proposed method uses three types of specific collocation pattern to construct the recognition algorithm based on fixed sentiment terms. These feature term sets are gradually updated by relevance feedbacks from the users which based on incremental tf-idf model. Comparison is done between the traditional method and fixed sentiment terms model. All tests showed the proposed method gets a higher efficiency and accuracy rate of the emotion classifier.