Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring agreement in medical informatics reliability studies
Journal of Biomedical Informatics
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
The reliability of a dialogue structure coding scheme
Computational Linguistics
Recognizing subjectivity: a case study in manual tagging
Natural Language Engineering
Inter-coder agreement for computational linguistics
Computational Linguistics
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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An affective text may be judged to belong to multiple affect categories as it may evoke different affects with varying degree of intensity. For affect classification of text, it is often required to annotate text corpus with affect categories. This task is often performed by a number of human judges. This paper presents a new agreement measure inspired by Kappa coefficient to compute inter-annotator reliability when the annotators have freedom to categorize a text into more than one class. The extended reliability coefficient has been applied to measure the quality of an affective text corpus. An analysis of the factors that influence corpus quality has been provided.