基于机器学习的网络购物产品评价情感分析模型研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI:10.1109/ICAICA52286.2021.9498066

Yifan Xu, Yong Ren

{"title":"基于机器学习的网络购物产品评价情感分析模型研究","authors":"Yifan Xu, Yong Ren","doi":"10.1109/ICAICA52286.2021.9498066","DOIUrl":null,"url":null,"abstract":"Based on the sentiment analysis of commodity evaluation text, a self-updating iterative algorithm is proposed to solve the problem of the mismatch between commodity evaluation and scoring, and the experiment proves that the algorithm is simple and efficient, and the accuracy of commodity evaluation can reach more than 99.17%.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Sentiment Analysis Model of Online Shopping Product Evaluation Based on Machine Learning\",\"authors\":\"Yifan Xu, Yong Ren\",\"doi\":\"10.1109/ICAICA52286.2021.9498066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the sentiment analysis of commodity evaluation text, a self-updating iterative algorithm is proposed to solve the problem of the mismatch between commodity evaluation and scoring, and the experiment proves that the algorithm is simple and efficient, and the accuracy of commodity evaluation can reach more than 99.17%.\",\"PeriodicalId\":121979,\"journal\":{\"name\":\"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"volume\":\"137 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAICA52286.2021.9498066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICA52286.2021.9498066","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

在对商品评价文本进行情感分析的基础上，提出了一种自更新迭代算法来解决商品评价与评分不匹配的问题，实验证明该算法简单高效，商品评价准确率可达到99.17%以上。

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

Research on Sentiment Analysis Model of Online Shopping Product Evaluation Based on Machine Learning

Based on the sentiment analysis of commodity evaluation text, a self-updating iterative algorithm is proposed to solve the problem of the mismatch between commodity evaluation and scoring, and the experiment proves that the algorithm is simple and efficient, and the accuracy of commodity evaluation can reach more than 99.17%.

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

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

自引率

0.00%

发文量