Title :
BBS Sentiment Classification Based on Word Polarity
Author :
Jie, XShen ; Xin, Fan ; Wen, Shen ; Quan-Xun, Ding
Author_Institution :
Inf. Eng. Coll., Yangzhou Univ., Yangzhou
Abstract :
Sentiment classification is an applied technology with great significance. It can help people find right reviews in a more efficient way. In this paper, we present a novel efficient method for BBS sentiment classification. Through extracting sentiment-bearing words from WordNet using the maximum entropy, a ranking criterion based on a function of the probability of having Polarity or not is introduced. The words with polarity are selected as features, which are processed with SVM classifier at the following step. The experimental results show that our method achieves high performance.
Keywords :
classification; entropy; probability; support vector machines; word processing; BBS sentiment classification; SVM classifier; WordNet; maximum entropy; probability; ranking criterion; sentiment-bearing words; word polarity; Data mining; Educational institutions; Entropy; Feature extraction; Frequency; Motion pictures; Natural languages; Probability distribution; Support vector machine classification; Support vector machines; feature selection; identify; maximum entropy; sentiment classification;
Conference_Titel :
Computer Engineering and Technology, 2009. ICCET '09. International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-3334-6
DOI :
10.1109/ICCET.2009.13
