了解移动银行中的投诉行为:对情绪驱动因素的心理和基于人工智能的分析
Le Li1, Wei Chong Choo2, Rony Kumar Datta3
1School of Business and Economics, Universiti Putra Malaysia, Malaysia; School of Management Engineering, Anhui Institute of Information Technology, Wuhu, China.
Acta psychologica
|February 10, 2026
概括
情绪智能和人工智能准确地分类移动银行问题. 负面情绪会增加报告,而信任和愤怒会减少,指导情感智能服务设计.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 人与计算机的交互
背景情况:
- 移动银行应用程序产生了大量的用户反.
- 了解用户的情绪对于服务改进至关重要.
- 现有的方法缺乏情绪智能和人工智能的整合来进行问题分类.
研究的目的:
- 使用人工智能和情绪智能对零售移动银行应用程序中用户报告的问题进行分类.
- 检查用户情绪与问题报告行为之间的关联.
- 为设计情感智能银行服务提供见解.
主要方法:
- 从美国银行移动银行应用程序中利用了超过216,000个用户评论.
- 使用NRC情感词典提取了情感和八个情感维度.
- 使用K-Means集群用于问题识别和机器学习/深度学习模型 (MLP,CNN,RNN) 进行预测,准确度>93%.
主要成果:
- 机器学习模型在预测问题类型方面实现了超过93%的准确性.
- 负面情绪与问题报告的增加有很强的相关性.
- 诸如信任和愤怒之类的情绪与报告问题的可能性较低有关.
结论:
- 情绪智能和人工智能有效地分类移动银行用户问题.
- 用户的情绪显著影响问题报告行为.
- 研究结果为开发情感智能数字金融服务提供了实用见解,并提升了对技术介导互动中情感行为的理解.
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