Analysts aren't machines: Inferring frustration through visualization interaction
Analysts aren't machines: Inferring frustration through visualization interaction
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分析师不是机器：通过可视化交互推断挫败感
DOI：
10.1109/vast.2011.6102473
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Xiaoyu Wang
中科院分区：
文献类型：
--
作者：
Lane Harrison;Wenwen Dou;Aidong Lu;W. Ribarsky;Xiaoyu Wang
最近的工作在视觉分析探讨了在何种程度上的信息分析行动和推理可以推断出互动。然而，这些方法通常依赖于人类而不是自动提取技术。然而，很少有人讨论用户在与可视化界面交互时的挫折感。我们证明了自动提取用户挫折是可能的动作级可视化交互日志。描述了一个实验，它收集的数据，准确地反映了用户的情感转变和相应的交互序列。然后，这些数据被用于建立隐马尔可夫模型（HHALDEN），该模型在统计上将交互事件与挫折联系起来。使用标准的机器学习评估方法测试了Hacker在预测用户挫折方面的能力。由此产生的分类器作为一个合适的预测用户的挫折，在不同的用户和数据集执行类似。
Recent work in visual analytics has explored the extent to which information regarding analyst action and reasoning can be inferred from interaction. However, these methods typically rely on humans instead of automatic extraction techniques. Futhermore, there is little discussion regarding the role of user frustration when interacting with a visual interface. We demonstrate that automatic extraction of user frustration is possible given action-level visualization interaction logs. An experiment is described which collects data that accurately reflects user emotion transitions and corresponding interaction sequences. This data is then used in building HiddenMarkov Models (HMMs) which statistically connect interaction events with frustration. The capabilities of HMMs in predicting user frustration are tested using standard machine learning evaluation methods. The resulting classifer serves as a suitable predictor of user frustration that performs similarly across different users and datasets.
DOI：
10.1016/0005-7916(94)90063-9
发表时间：
1994-03-01
影响因子：
1.8
作者：
BRADLEY, MM;LANG, PJ
通讯作者：
LANG, PJ