DocumentCode :
525467
Title :
Automatic positive sentiment word extraction for Chinese text classification
Author :
Yu, Zhen´gang ; Zhen, Ning ; Xu, Ming
Author_Institution :
Dept.of Comput. Sci., Hangzhou Dianzi Univ., Hangzhou, China
Volume :
1
fYear :
2010
fDate :
25-27 June 2010
Abstract :
Sentiment analysis aims to predict sentiment tendency automatically. Traditional methods tackling this problem are mostly based on supervised learning,but it is time-consuming and uneasy to extendable. In this paper,we provide a novel method of sentiment analysis based on un-supervised learning together with some language rules. It is no necessary to have a positive sentiment dictionary beforehand as we can build it automatically during processing the comments. By this positive sentiment dictionary,it provides an efficient way to classify the product reviews. The methodology presented is easy to extend due to its un-domain-dependency. As we can see,the experiment result obtained shows its promising application.
Keywords :
classification; natural language processing; text analysis; unsupervised learning; Chinese text classification; automatic positive sentiment word extraction; sentiment analysis; unsupervised learning; Computer science; Data mining; Dictionaries; Entropy; Humans; Information analysis; Machine learning; Supervised learning; Text categorization; Unsupervised learning; Chinese; product reviews; sentiment analysis; unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Design and Applications (ICCDA), 2010 International Conference on
Conference_Location :
Qinhuangdao
Print_ISBN :
978-1-4244-7164-5
Electronic_ISBN :
978-1-4244-7164-5
Type :
conf
DOI :
10.1109/ICCDA.2010.5541454
Filename :
5541454
Link To Document :
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