Title :
Chinese text emotion classification based on emotion dictionary
Author :
Li, Jun ; Xu, Yuemei ; Xiong, Hao ; Wang, Yan
Author_Institution :
Nat. Network New Media Eng. Res. Center, Chinese Acad. of Sci., Beijing, China
Abstract :
Recently, much work have been done on text emotion classification. However, they mainly focused on the emotions expressed by authors instead of the readers. In addition, researches on simplified Chinese text emotion classification are extremely less. In this paper, we proposed a simplified Chinese text emotion classification based on readers´ emotions. Mass of documents with readers´ emotion tag are used as raw text sets, and Vector Space Model is used to represent each document. An emotion dictionary is created semi-automatically by using WordNet to build text vectors. We then train a Support Vector Machine classifier on preprocessed data with four emotion classes, and compared the predicate results with that from Naive Bayes classifier. Experiment results indicate that our approach performs much better on classify accuracy and efficiency.
Keywords :
support vector machines; text analysis; Chinese text emotion classification; Naive Bayes classifier; WordNet; emotion dictionary; support vector machine; text vectors; vector space model; Accuracy; Dictionaries; Feature extraction; Information processing; Support vector machine classification; Training; Emotion Classification; Mutual Information; Support Vector Machine; Vector Space Model;
Conference_Titel :
Web Society (SWS), 2010 IEEE 2nd Symposium on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6356-5
DOI :
10.1109/SWS.2010.5607460