Affective computing
Emotion recognition from text using semantic labels and separable mixture models
ACM Transactions on Asian Language Information Processing (TALIP)
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building emotion lexicon from weblog corpora
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
IEEE Transactions on Affective Computing
A Regression Approach to Music Emotion Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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Affective norms for the words is an important issue in textual emotion recognition application. One problem with existing research is that several studies were rated with a large number of participants, making it difficult to apply to different languages. Moreover, difference in culture across different ethnic groups makes the language/culture-specific affective norms not directly translatable to the applications using different languages. To overcome these problems, in this paper, a new approach to semi-automatic labeling of Chinese affective norms for the 1,034 words included in the affective norms for English words (ANEW) is proposed which use a rating of small number of Chinese words from ontology concept clusters with a regression-based approach for transforming the 1,034 English words' ratings to the corresponding Chinese words' ratings. The experimental result demonstrated that the proposed approach can be practically implemented and provide adequate results.