放弃虚假的有利反馈，以获得更好的情绪分析

Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2021-10-22 DOI:10.1145/3501409.3501672

Qiankun Su, Zhida Feng, Wujin Sun, Lizhen Chen

{"title":"放弃虚假的有利反馈，以获得更好的情绪分析","authors":"Qiankun Su, Zhida Feng, Wujin Sun, Lizhen Chen","doi":"10.1145/3501409.3501672","DOIUrl":null,"url":null,"abstract":"Fake favorable feedback is a big obstacle for customers to get better experiences on shopping websites. As far as we know, the effects of fake favorable feedback on natural language processing models have not been reported. To investigate the distraction of fake feedback, this paper developed a method to resolute fake feedback (RFF). The proposed RFF first analyzes the tokenizes of feedback, and then several short texts are generated to replace some long texts. Experimental results on the raw data and processed data show the effectiveness of our proposal.","PeriodicalId":191106,"journal":{"name":"Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dropping Fake Favorable Feedback for Better Sentiment Analysis\",\"authors\":\"Qiankun Su, Zhida Feng, Wujin Sun, Lizhen Chen\",\"doi\":\"10.1145/3501409.3501672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fake favorable feedback is a big obstacle for customers to get better experiences on shopping websites. As far as we know, the effects of fake favorable feedback on natural language processing models have not been reported. To investigate the distraction of fake feedback, this paper developed a method to resolute fake feedback (RFF). The proposed RFF first analyzes the tokenizes of feedback, and then several short texts are generated to replace some long texts. Experimental results on the raw data and processed data show the effectiveness of our proposal.\",\"PeriodicalId\":191106,\"journal\":{\"name\":\"Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3501409.3501672\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3501409.3501672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

虚假的好评反馈是顾客在购物网站上获得更好体验的一大障碍。据我们所知，假有利反馈对自然语言处理模型的影响尚未见报道。为了研究虚假反馈的干扰，本文提出了一种消除虚假反馈的方法。提出的RFF首先分析反馈的标记化，然后生成几个短文本来替换一些长文本。在原始数据和处理数据上的实验结果表明了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Dropping Fake Favorable Feedback for Better Sentiment Analysis

Fake favorable feedback is a big obstacle for customers to get better experiences on shopping websites. As far as we know, the effects of fake favorable feedback on natural language processing models have not been reported. To investigate the distraction of fake feedback, this paper developed a method to resolute fake feedback (RFF). The proposed RFF first analyzes the tokenizes of feedback, and then several short texts are generated to replace some long texts. Experimental results on the raw data and processed data show the effectiveness of our proposal.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering

自引率

0.00%

发文量