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.

中文翻译：

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公众对网络P2P借贷应用的看法

本研究通过采用主题建模和情绪分析，研究了印度 P2P 借贷应用程序评论中传达的重要主题和客户情绪。考虑的应用程序包括 LenDenClub、Faircent、i2ifunding、India Money Mart 和 Lendbox。使用潜在狄利克雷分配，我们识别并标记了 11 个主题：应用程序、文档、默认、登录、拒绝、服务、CIBIL、OTP、返回、接口和取款。情绪分析工具 VADER 显示，大多数用户对这些应用持积极态度。我们还对这五个应用程序进行了总体比较和特定主题的比较。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。逻辑回归展示了所有三种特征提取技术的高精度：词袋、词频-逆文档频率和散列。该研究帮助借款人和贷款人选择最合适的应用程序，并支持 P2P 借贷平台认识自己的优势和劣势。