Abstract
This paper presents a cognitive-based emotion classifier of Chinese vocabulary, which inherits the advantages of traditional statistical linguistics model. The concept of cognitive prototype theory in cognitive linguistics was applied for the filter of text characteristics, while HowNet, which can provide the interface of the calculation of semantic similarity, and The Corpus Annotation of Harbin Institute of Technology(HIT) were also used in this classifier, accordingly this paper build a new classification model. It is used in binary classification of word emotion and the experimental results show that the accuracy of the classifier for the word emotion was significantly higher than the traditional classifier based solely on statistics, and on the other hand it greatly reduced the computational cost. Although the recall rate was slightly lower, it can be solved with improvement suggestion in the end of this paper.
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