通过机器学习评估基本情绪:对数量基本情绪和算法的比较分析.
概括
机器学习模型有效地使用快乐,兴奋和主导 (PAD) 评分对情绪进行集群. 决策树准确地将PAD分数映射到数据集中的情绪状态,增强了情绪建模理解.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 情感计算是一种情感计算.
背景情况:
- 情绪自我报告和建模是复杂的.
- 生理信号和维度情感模型 (快乐,兴奋,主导 - PAD) 提供了替代方法.
- 机器学习 (ML) 可以分析复杂的数据集以识别情绪.
研究的目的:
- 开发和评估基于PAD分数的情绪聚类的ML算法.
- 将PAD分数映射到离散的情绪状态 (例如,快乐,悲伤).
- 使用已建立的情感数据集来评估算法性能.
主要方法:
- 利用了使用生理信号 (DEAP) 和国际情感图像系统 (IAPS) 的情感分析数据集的数据集.
- 应用了肘部方法来确定最佳集群数量 (4-8).
- 对比了九个ML算法,重点是决策树和支持矢量机器 (SVM).
主要成果:
- 决策树,多项式SVM和线性SVM显示了准确的结果.
- 决策树在DEAP和IAPS数据集中识别情绪集群方面表现出一致的效率.
- 这项研究发现了由于每种情绪的数据有限而导致的潜在过拟合.
结论:
- ML,特别是决策树,可以有效地基于PAD维度来模拟和集群情绪.
- 这些发现有助于对情绪自我报告和计算情绪建模的细微理解.
- 这种方法提供了一种基于数据的情绪分类方法.
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