Application of Chinese sentiment categorization to digital products reviews

Author

Hongying Zan ; Kuizhong Kou ; JiaLe Tian ; Sin, R.

Author_Institution

Coll. of Inf. & Eng., ZhengZhou Univ., Zhengzhou, China

fYear

2010

fDate

21-23 Aug. 2010

Firstpage

1

Lastpage

5

Abstract

Sentiment categorization have been widely explored in many fields, such as government policy, information monitoring, product tracking, etc. This paper adopts k-NN, Naive Bayes and SVM classifiers to categorize sentiments contained in on-line Chinese reviews on digital products. Our experimental results show that combining the words and phrases with sentiment orientation as hybrid features, SWM classifier achieves an accuracy of 96,47%, which is words of all parts of speech as features.

Keywords

classification; learning (artificial intelligence); natural language processing; support vector machines; , information monitoring; Chinese sentiment categorization; Naive Bayes classifiers; SWM classifier; digital products reviews; government policy; k-NN classifiers; product tracking; Book reviews; Classification algorithms; Conferences; Monitoring; Semantics; Speech; Support vector machines; Chinese information processing; Naïve Bayes; SWM; k-NN; sentiment categorization;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Language Processing and Knowledge Engineering (NLP-KE), 2010 International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-6896-6

Type

conf

DOI

10.1109/NLPKE.2010.5587788

Filename

5587788