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

Volume

1

fYear

2009

fDate

22-24 Jan. 2009

Firstpage

352

Lastpage

356

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Engineering and Technology, 2009. ICCET '09. International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-3334-6

Type

conf

DOI

10.1109/ICCET.2009.13

Filename

4769487