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.

翻译：移动银行（m-banking）的快速增长，特别是在COVID-19疫情之后，重塑了金融行业。本研究分析了加拿大五家主要银行的移动银行应用在Google Play和iOS应用商店中的用户评论。通过情感分析和主题建模将评论分类为正面、中性或负面，从而突显用户偏好及需改进的领域。数据预处理使用Python语言处理工具NLTK完成，主题建模采用隐狄利克雷分布（LDA）方法。情感分析比较了多种方法，其中长短期记忆网络（LSTM）在iOS评论中达到82%的准确率，而多项式朴素贝叶斯在Google Play评论中达到77%的准确率。正面评论赞扬了应用的可用性、可靠性和功能特性，负面评论则指出了登录问题、系统故障以及对更新的不满。这是首个同时分析iOS与Google Play移动银行应用评论的研究，为应用的优势与不足提供了深入见解。研究结果强调了用户友好设计、稳定更新以及更优质客户服务的重要性。先进的文本分析方法为提升用户满意度和体验提供了可操作的改进建议。