Abstract
This thesis presents a method of detecting the emotion from the Chinese sentences by using document classification method. There are much work has been done to detect users’ emotion states from multimodal sources such as audio, gestures, and eye gazes over the last decade. But compare to research of emotion detection in multimodal fields, emotion detection from text is still not mature and requires more improvements to be assembled as practical applications. We used the micro-blog as the data source, because the data type of micro-blog is similar to the sentence, and the emotion icon will be annotated when user write the article. The Point-wise Mutual Information (PMI) was employed to help us to calculate the strength between the word and emotion icon. The threshold was designed to avoid choosing the feature which has the similar PMI vale. We reduce the feature dimension by using the Chinese thesaurus. Finally, we use the support vector machine to deal with classification task. The experiments show that the best performance of our method is more than 85%. We find out that the small feature set size was also efficient in the classification task. Our method maybe can be applied to other research of document classification problem.