{"title":"基于情感的高价值评论的子句分析","authors":"Akiyo Nadamoto, Kazuhiro Akiyama, T. Kumamoto","doi":"10.26421/JDI1.4-4","DOIUrl":null,"url":null,"abstract":"Today, huge numbers of reviews are posted on the internet. Online shoppers often refer to reviews written about the products. A review has a star rating that represents what other people think about the product. However, the star rating is not always appropriate for evaluating the product. High-value reviews that affect the users' willingness to buy are independent of the number of stars in ratings. High-value reviews are those from which people find useful information those regarded as good reviews. As described in this paper, we investigated the relation between high-value reviews and the sentiment (positive/negative/neutral) of their clauses based on four hypotheses. We extract characteristics of high-value reviews based on our results. Furthermore, we propose a classification method that classifies clause level sentiment from reviews.","PeriodicalId":232625,"journal":{"name":"J. Data Intell.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clause-level Analysis High-value Reviews based on Sentiment\",\"authors\":\"Akiyo Nadamoto, Kazuhiro Akiyama, T. Kumamoto\",\"doi\":\"10.26421/JDI1.4-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today, huge numbers of reviews are posted on the internet. Online shoppers often refer to reviews written about the products. A review has a star rating that represents what other people think about the product. However, the star rating is not always appropriate for evaluating the product. High-value reviews that affect the users' willingness to buy are independent of the number of stars in ratings. High-value reviews are those from which people find useful information those regarded as good reviews. As described in this paper, we investigated the relation between high-value reviews and the sentiment (positive/negative/neutral) of their clauses based on four hypotheses. We extract characteristics of high-value reviews based on our results. Furthermore, we propose a classification method that classifies clause level sentiment from reviews.\",\"PeriodicalId\":232625,\"journal\":{\"name\":\"J. Data Intell.\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Data Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26421/JDI1.4-4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Data Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26421/JDI1.4-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clause-level Analysis High-value Reviews based on Sentiment
Today, huge numbers of reviews are posted on the internet. Online shoppers often refer to reviews written about the products. A review has a star rating that represents what other people think about the product. However, the star rating is not always appropriate for evaluating the product. High-value reviews that affect the users' willingness to buy are independent of the number of stars in ratings. High-value reviews are those from which people find useful information those regarded as good reviews. As described in this paper, we investigated the relation between high-value reviews and the sentiment (positive/negative/neutral) of their clauses based on four hypotheses. We extract characteristics of high-value reviews based on our results. Furthermore, we propose a classification method that classifies clause level sentiment from reviews.