使用自然语言处理的Web服务客户评论摘要

Hema Priya N, S. S, R. N, Adithya Harish S M
{"title":"使用自然语言处理的Web服务客户评论摘要","authors":"Hema Priya N, S. S, R. N, Adithya Harish S M","doi":"10.4108/eai.7-12-2021.2314556","DOIUrl":null,"url":null,"abstract":". Customers can submit reviews for numerous products on websites like Amazon and Flipkart. As e-commerce grows in popularity, so does the quantity of consumer reviews that a product receives. A single product may have hundreds of thousands of reviews, each of which may be lengthy and repetitious. As a result, computerised review summarization offers a lot of potential for assisting buyers in making quick selections about certain items. Because a single manufacturer may sell a variety of items. It is also beneficial for manufacturers to keep track of customer feedback and comments. The process of creating a summary from review sentences is known as review summarising.In this project, given a product review, a shorter version of the review is created while the sentiment and points are preserved. The tone of the review will also be determined, and a summary of sample favourable and bad product reviews will be generated. Web scraping is used to collect reviews from popular ecommerce websites. Natural Language Processing Toolkit and neural networks such as RNN (Recurrent Neural Network) are used to summarise. The RNN architecture is combined with the Seq2Seq model, which is an encoder-decoder architecture. The highest accuracy for sentiment analysis on Amazon Fine Food Reviews was found to be 91%.","PeriodicalId":20712,"journal":{"name":"Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Summarization of Customer Reviews in Web Services using Natural Language Processing\",\"authors\":\"Hema Priya N, S. S, R. N, Adithya Harish S M\",\"doi\":\"10.4108/eai.7-12-2021.2314556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". Customers can submit reviews for numerous products on websites like Amazon and Flipkart. As e-commerce grows in popularity, so does the quantity of consumer reviews that a product receives. A single product may have hundreds of thousands of reviews, each of which may be lengthy and repetitious. As a result, computerised review summarization offers a lot of potential for assisting buyers in making quick selections about certain items. Because a single manufacturer may sell a variety of items. It is also beneficial for manufacturers to keep track of customer feedback and comments. The process of creating a summary from review sentences is known as review summarising.In this project, given a product review, a shorter version of the review is created while the sentiment and points are preserved. The tone of the review will also be determined, and a summary of sample favourable and bad product reviews will be generated. Web scraping is used to collect reviews from popular ecommerce websites. Natural Language Processing Toolkit and neural networks such as RNN (Recurrent Neural Network) are used to summarise. The RNN architecture is combined with the Seq2Seq model, which is an encoder-decoder architecture. The highest accuracy for sentiment analysis on Amazon Fine Food Reviews was found to be 91%.\",\"PeriodicalId\":20712,\"journal\":{\"name\":\"Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eai.7-12-2021.2314556\",\"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 First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eai.7-12-2021.2314556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

. 消费者可以在亚马逊和Flipkart等网站上提交对众多产品的评论。随着电子商务的普及,消费者对产品的评论也越来越多。单个产品可能有数十万条评论,每条评论都可能很长且重复。因此,计算机化的评论总结为帮助买家快速选择特定商品提供了很大的潜力。因为一个制造商可以销售各种各样的产品。这也有利于制造商跟踪客户的反馈和意见。从回顾句子中创建总结的过程被称为回顾总结。在这个项目中,给定一个产品评论,将创建一个较短的评论版本,同时保留观点和要点。审查的语气也将被确定,并将生成样品有利和不良产品评论的摘要。网络抓取用于从流行的电子商务网站收集评论。自然语言处理工具包和神经网络,如RNN(递归神经网络)用于总结。RNN体系结构与Seq2Seq模型相结合,后者是一种编码器-解码器体系结构。在亚马逊美食评论中,情感分析的最高准确率为91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Summarization of Customer Reviews in Web Services using Natural Language Processing
. Customers can submit reviews for numerous products on websites like Amazon and Flipkart. As e-commerce grows in popularity, so does the quantity of consumer reviews that a product receives. A single product may have hundreds of thousands of reviews, each of which may be lengthy and repetitious. As a result, computerised review summarization offers a lot of potential for assisting buyers in making quick selections about certain items. Because a single manufacturer may sell a variety of items. It is also beneficial for manufacturers to keep track of customer feedback and comments. The process of creating a summary from review sentences is known as review summarising.In this project, given a product review, a shorter version of the review is created while the sentiment and points are preserved. The tone of the review will also be determined, and a summary of sample favourable and bad product reviews will be generated. Web scraping is used to collect reviews from popular ecommerce websites. Natural Language Processing Toolkit and neural networks such as RNN (Recurrent Neural Network) are used to summarise. The RNN architecture is combined with the Seq2Seq model, which is an encoder-decoder architecture. The highest accuracy for sentiment analysis on Amazon Fine Food Reviews was found to be 91%.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信