在B2C网站上对用户情绪体验的分类,使用红外热成像
Lanxin Li1, Wenzhe Tang1, Han Yang2
1School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
红外热成像 (IRTI) 在网站交互期间非侵入性地分类用户的情绪. 这种方法可以准确区分积极和消极的情绪状态,为情绪分析提供了一个新的工具.
科学领域:
- 人与计算机的交互
- 情感计算是一种情感计算.
- 心理生理学 心理生理学
背景情况:
- 传统的生理信号获取用于情绪分析往往是侵入性的,可以产生不准确的结果.
- 需要使用非侵入性方法来准确捕捉人与计算机交互期间的情感体验.
- 红外热成像 (IRTI) 是一种有希望的非侵入性方法来检测情绪.
研究的目的:
- 研究IRTI在与企业对消费者 (B2C) 网站互动时对用户情绪体验进行分类方面的有效性.
- 探索面部热模式与自我报告的情绪状态之间的关系.
- 识别关键的感兴趣的面部区域 (ROI) 和用于情绪分类的机器学习特征.
主要方法:
- 在24名参与者与B2C网站互动时,他们捕获了面部热图像,操纵了可用性和美学.
- 机器学习技术,包括支持矢量机器 (SVM),用于情感分类.
- 参与者的自我评估被用作验证分类结果的基本真相.
主要成果:
- 观察到情绪价值的显著波动,允许将其分类为积极和消极状态.
- 参与者的兴奋水平保持一致,支持基于价值的分类.
- SVM模型在区分基线和情绪状态方面表现出强的表现.
- 确定了关键的面部ROI和用于情绪分类的有效机器学习特征.
结论:
- IRTI提供了一种可行的非侵入性方法,用于分析用户在网站互动期间的情绪体验.
- 这项研究确立了用户情绪和IRTI之间的重要联系,推进了情绪分析研究.
- 这项研究扩大了IRTI在理解人类情绪状态方面的应用范围.
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