基于证券应用程序元素的用户情绪分析
Minji Kim1, Subeen Kim2, Yoonha Park2
1Department of Artificial Intelligence, Kyung Hee University, Yongin 17104, Republic of Korea.
Behavioral sciences (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了基于方面的情绪分析 (ABSA),通过分析用户评论来改进韩国移动证券应用程序. ABSA揭示了特定用户对登录和交易等功能的反,提供了比传统方法更深入的设计见解.
科学领域:
- 自然语言处理自然语言处理.
- 移动应用程序设计
- 情绪分析 情绪分析
背景情况:
- 由于复杂性,设计移动证券应用程序存在重大挑战.
- 分析在线用户评论对于应用程序的改进至关重要.
- 对韩国文本情感分析的深度学习应用尚未得到充分探索.
研究的目的:
- 探索基于方面的情绪分析 (ABSA) 分析韩国证券申请审查.
- 在这些应用程序中识别关键的设计元素和用户情绪.
- 展示ABSA作为传统用户研究的可扩展和具有成本效益的替代方案.
主要方法:
- 利用基于方面的情绪分析 (ABSA) 对韩国证券申请的基于文本的用户评论数据.
- 应用的技术包括点向相互信息 (PMI),奇点值分解 (SVD) 和Word2Vec.
- 确定了诸如"更新"",屏幕"",图表"",登录"",访问"",身份验证"",帐户"和"交易"等关键方面.
主要成果:
- 与整体评级相比,ABSA对用户情绪提供了更深入的见解.
- 确定了用户不满的特定领域,即使在一般积极的评论中也是如此.
- 突出了影响移动证券应用程序用户体验的关键因素.
结论:
- ABSA是一种有效的方法,用于在移动应用程序设计中揭示微妙的用户情绪.
- 这种方法为证券应用领域的用户研究提供了一个可扩展和具有成本效益的解决方案.
- 这些发现可以为移动证券平台的未来设计改进提供信息.
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