Data-Driven Refinement of a Probabilistic Model of User Affect
Data-Driven Refinement of a Probabilistic Model of User Affect
复制标题
用户情感概率模型的数据驱动细化
DOI：
10.1007/11527886_7
复制
发表时间：
2005
期刊：
影响因子：
37.8
通讯作者：
H. Maclaren
中科院分区：
文献类型：
--
作者：
C. Conati;H. Maclaren
我们介绍了我们的工作的进一步发展，使用来自真实用户的数据来建立基于动态贝叶斯网络(DBN)的用户情感的概率模型，并设计用于检测多种情感。我们提供了对先前评估识别的不准确的分析和解决方案；改进了模型对事件的评估，以更接近地反映真实用户的评估。我们的发现让我们挑战了之前的假设，并对进一步改进的方向提出了见解。
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to detect multiple emotions. We present analysis and solutions for inaccuracies identified by a previous evaluation; refining the model's appraisals of events to reflect more closely those of real users. Our findings lead us to challenge previously made assumptions and produce insights into directions for further improvement.