DocumentCode
3499469
Title
Sentiment Classification for Chinese Reviews Using Machine Learning Methods Based on String Kernel
Author
Zhang, Changli ; Zuo, Wanli ; Peng, Tao ; He, Fengling
Author_Institution
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun
Volume
2
fYear
2008
fDate
11-13 Nov. 2008
Firstpage
909
Lastpage
914
Abstract
Sentiment classification aims at mining reviews of people for a certain event´s topic or product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers´ opinions from the web information. Automatic opinion mining will benefit to both decision maker and ordinary people. Up to now, it is still a complicated task with great challenge. There are mainly two types of approaches for sentiment classification, machine learning methods and semantic orientation methods. Though some pioneer researches explored the approaches for English reviews classification, few jobs have been done on sentiment classification for Chinese reviews. The machine learning approach Based on string kernel for sentiment classification on reviews written in Chinese was proposed in this paper. Data experiment shows the capability of this approach.
Keywords
Internet; classification; data mining; decision making; learning (artificial intelligence); Chinese reviews; World Wide Web; automatic opinion mining; decision making; machine learning; sentiment classification; string kernel; Educational institutions; Information analysis; Information retrieval; Kernel; Learning systems; Machine learning; Machine learning algorithms; Mutual information; Support vector machines; Text categorization; machine learning; opinion mining; semantic orientation; sentiment classification; string kernel;
fLanguage
English
Publisher
ieee
Conference_Titel
Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on
Conference_Location
Busan
Print_ISBN
978-0-7695-3407-7
Type
conf
DOI
10.1109/ICCIT.2008.51
Filename
4682362
Link To Document