Emotion Cause Detection with Linguistic Construction in Chinese Weibo Text

To identify the cause of emotion is a new challenge for researchers in nature language processing. Currently, there is no existing works on emotion cause detection from Chinese micro-blogging (Weibo) text. In this study, an emotion cause annotated corpus is firstly designed and developed through annotating the emotion cause expressions in Chinese Weibo Text. Up to now, an emotion cause annotated corpus which consists of the annotations for 1,333 Chinese Weibo is constructed. Based on the observations on this corpus, the characteristics of emotion cause expression are identified. Accordingly, a rulebased emotion cause detection method is developed which uses 25 manually complied rules. Furthermore, two machine learning based cause detection methods are developed including a classification-based method using support vector machines and a sequence labeling based method using conditional random fields model. It is the largest available resources in this research area. The experimental results show that the rule-based method achieves 68.30% accuracy rate. Furthermore, the method based on conditional random fields model achieved 77.57% accuracy which is 37.45% higher than the reference baseline method. These results show the effectiveness of our proposed emotion cause detection method.