{"title":"在线产品评论和新闻媒体文章中有效情感-目标对的检测","authors":"Svitlana Vakulenko, A. Weichselbraun, A. Scharl","doi":"10.1109/WI.2016.0024","DOIUrl":null,"url":null,"abstract":"This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic structure of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"190 12 1","pages":"97-104"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Detection of Valid Sentiment-Target Pairs in Online Product Reviews and News Media Articles\",\"authors\":\"Svitlana Vakulenko, A. Weichselbraun, A. Scharl\",\"doi\":\"10.1109/WI.2016.0024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic structure of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.\",\"PeriodicalId\":6513,\"journal\":{\"name\":\"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)\",\"volume\":\"190 12 1\",\"pages\":\"97-104\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2016.0024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2016.0024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of Valid Sentiment-Target Pairs in Online Product Reviews and News Media Articles
This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic structure of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.
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