公众对网络 P2P 借贷应用的看法

IF 5.1 3区 管理学 Q1 BUSINESS
Sahiba Khan, Ranjit Singh, H. Kent Baker, Gomtesh Jain
{"title":"公众对网络 P2P 借贷应用的看法","authors":"Sahiba Khan, Ranjit Singh, H. Kent Baker, Gomtesh Jain","doi":"10.3390/jtaer19010027","DOIUrl":null,"url":null,"abstract":"This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.","PeriodicalId":46198,"journal":{"name":"Journal of Theoretical and Applied Electronic Commerce Research","volume":"18 1","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Public Perception of Online P2P Lending Applications\",\"authors\":\"Sahiba Khan, Ranjit Singh, H. Kent Baker, Gomtesh Jain\",\"doi\":\"10.3390/jtaer19010027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.\",\"PeriodicalId\":46198,\"journal\":{\"name\":\"Journal of Theoretical and Applied Electronic Commerce Research\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Theoretical and Applied Electronic Commerce Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.3390/jtaer19010027\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Theoretical and Applied Electronic Commerce Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.3390/jtaer19010027","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0

摘要

本研究通过采用主题建模和情感分析,研究了印度 P2P 借贷应用程序(应用程序)评论中传达的重要主题和客户情感。研究对象为 LenDenClub、Faircent、i2ifunding、India Money Mart 和 Lendbox。通过使用潜在德里希特分配(Latent Dirichlet Allocation),我们确定并标记了 11 个主题:申请、文件、默认、登录、拒绝、服务、CIBIL、OTP、回报、界面和提款。情感分析工具 VADER 显示,大多数用户对这些应用程序持积极态度。我们还对五款应用程序的整体情况和特定主题进行了比较。总体而言,LenDenClub 的正面评价比例最高。我们还比较了六种机器学习模型的预测能力。Logistic 回归在所有三种特征提取技术(词包、词频-反文档频率和散列)中都表现出较高的准确性。这项研究有助于借款人和贷款人选择最合适的应用程序,并支持 P2P 网络借贷平台认识自身的优缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Public Perception of Online P2P Lending Applications
This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.
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
CiteScore
9.50
自引率
3.60%
发文量
67
期刊介绍: The Journal of Theoretical and Applied Electronic Commerce Research (JTAER) has been created to allow researchers, academicians and other professionals an agile and flexible channel of communication in which to share and debate new ideas and emerging technologies concerned with this rapidly evolving field. Business practices, social, cultural and legal concerns, personal privacy and security, communications technologies, mobile connectivity are among the important elements of electronic commerce and are becoming ever more relevant in everyday life. JTAER will assist in extending and improving the use of electronic commerce for the benefit of our society.
×
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