分析五大银行移动应用评价，找出用户痛点与改进方向
Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews
- 用LDA和情感分析挖掘应用评论主题与情绪
- LSTM在iOS评论中准确率达82%，优于其他模型
- 适合关注金融APP用户体验优化的从业者
新冠疫情后移动银行业务迅速发展，重塑金融行业。本研究分析了加拿大五大银行在Google Play和iOS应用商店的用户评论，采用NLTK进行数据预处理，使用潜变量狄利克雷分配（LDA）进行主题建模，并比较多种情感分析方法。结果显示，LSTM在iOS评论上达到82%准确率，多项式朴素贝叶斯在Google Play上为77%。正面评论多赞扬易用性、稳定性和功能，负面评论则集中反映登录问题、程序错误及对更新的不满。这是首个同时分析iOS与Google Play移动端银行应用评论的研究，揭示了应用优劣势，强调了用户友好设计、稳定更新和改善客服的重要性。先进文本分析为提升用户满意度提供可操作建议。
原文摘要 · Abstract (English)
The rapid growth of mobile banking (m-banking), especially after the COVID-19 pandemic, has reshaped the financial sector. This study analyzes consumer reviews of m-banking apps from five major Canadian banks, collected from Google Play and iOS App stores. Sentiment analysis and topic modeling classify reviews as positive, neutral, or negative, highlighting user preferences and areas for improvement. Data pre-processing was performed with NLTK, a Python language processing tool, and topic modeling used Latent Dirichlet Allocation (LDA). Sentiment analysis compared methods, with Long Short-Term Memory (LSTM) achieving 82\% accuracy for iOS reviews and Multinomial Naive Bayes 77\% for Google Play. Positive reviews praised usability, reliability, and features, while negative reviews identified login issues, glitches, and dissatisfaction with updates.This is the first study to analyze both iOS and Google Play m-banking app reviews, offering insights into app strengths and weaknesses. Findings underscore the importance of user-friendly designs, stable updates, and better customer service. Advanced text analytics provide actionable recommendations for improving user satisfaction and experience.
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