Natural Affect Data: Collection and Annotation
Natural Affect Data: Collection and Annotation
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DOI：
10.1007/978-1-4419-9625-1_5
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发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
Robinson, Peter
中科院分区：
文献类型：
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作者：
Afzal, Shazia;Robinson, Peter
情绪对健康的认知功能至关重要，并与学习和成就直接相关。因此，毫不奇怪，情感诊断构成了专家人际指导的一个重要方面。因此，基于计算机的学习环境寻求将这种人类师生互动的社会动态结合起来，以使计算机学习更有吸引力和更有效。情感计算领域的进步开启了从非语言表现中研究情感的可能性，并推动了一些实现自动情感推理的努力。然而，情感识别系统的开发和验证需要代表性数据作为基础事实。对于影响敏感技术的可行应用，并确保生态有效性，最好使用与环境相关的自然数据。本章报告了在一个学习场景中收集和随后注释数据的结果。讨论了数据收集过程中遇到的概念和方法问题，并确定了标签和注释的问题。它提供了一个综合的复杂性和挑战与情绪评估在自然情况下。
Emotions are crucial for healthy cognitive functioning and have direct relevance to learning and achievement. Not surprisingly then, affective diagnoses constitute a significant aspect of expert human mentoring. Consequently, computer-based learning environments seek to incorporate the social dynamics of such human teacher–learner interactions in order to make learning with computers more engaging and effective. Advances in the field of affective computing have opened the possibility of studying emotions from their nonverbal manifestations and have motivated several efforts towards realising automatic affect inference. However, development and validation of affect recognition systems requires representative data to serve as the ground-truth. For viable applications of affect-sensitive technology, and to ensure ecological validity, the use of context-relevant, naturalistic data is preferred. This chapter reports results from the collection and subsequent annotation of data obtained in a learning scenario. The conceptual and methodological issues encountered during data collection are discussed, and problems with labelling and annotation are identified. It provides an integrated account of the complexity and challenges associated with emotion assessment in naturalistic situations.