{"title":"面向中文文本分类的积极情感词自动提取","authors":"Zhen'gang Yu, N. Zhen, Ming Xu","doi":"10.1109/ICCDA.2010.5541454","DOIUrl":null,"url":null,"abstract":"Sentiment analysis aims to predict sentiment tendency automatically. Traditional methods tackling this problem are mostly based on supervised learning,but it is time-consuming and uneasy to extendable. In this paper,we provide a novel method of sentiment analysis based on un-supervised learning together with some language rules. It is no necessary to have a positive sentiment dictionary beforehand as we can build it automatically during processing the comments. By this positive sentiment dictionary,it provides an efficient way to classify the product reviews. The methodology presented is easy to extend due to its un-domain-dependency. As we can see,the experiment result obtained shows its promising application.","PeriodicalId":190625,"journal":{"name":"2010 International Conference On Computer Design and Applications","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Automatic positive sentiment word extraction for Chinese text classification\",\"authors\":\"Zhen'gang Yu, N. Zhen, Ming Xu\",\"doi\":\"10.1109/ICCDA.2010.5541454\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sentiment analysis aims to predict sentiment tendency automatically. Traditional methods tackling this problem are mostly based on supervised learning,but it is time-consuming and uneasy to extendable. In this paper,we provide a novel method of sentiment analysis based on un-supervised learning together with some language rules. It is no necessary to have a positive sentiment dictionary beforehand as we can build it automatically during processing the comments. By this positive sentiment dictionary,it provides an efficient way to classify the product reviews. The methodology presented is easy to extend due to its un-domain-dependency. As we can see,the experiment result obtained shows its promising application.\",\"PeriodicalId\":190625,\"journal\":{\"name\":\"2010 International Conference On Computer Design and Applications\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference On Computer Design and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCDA.2010.5541454\",\"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 International Conference On Computer Design and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCDA.2010.5541454","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic positive sentiment word extraction for Chinese text classification
Sentiment analysis aims to predict sentiment tendency automatically. Traditional methods tackling this problem are mostly based on supervised learning,but it is time-consuming and uneasy to extendable. In this paper,we provide a novel method of sentiment analysis based on un-supervised learning together with some language rules. It is no necessary to have a positive sentiment dictionary beforehand as we can build it automatically during processing the comments. By this positive sentiment dictionary,it provides an efficient way to classify the product reviews. The methodology presented is easy to extend due to its un-domain-dependency. As we can see,the experiment result obtained shows its promising application.