Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/NLPKE.2010.5587788 | NLPKE |
Keywords | DocType | Volume |
chinese information processing,k-nn,chinese sentiment categorization,learning (artificial intelligence),product tracking,government policy,naïve bayes,sentiment categorization,naive bayes classifiers,svm,swm classifier,digital products reviews,k-nn classifiers,classification,natural language processing,support vector machines,information monitoring,swm,classification algorithms,speech,naive bayes,information processing,learning artificial intelligence,part of speech,semantics | Conference | null |
Issue | ISSN | ISBN |
null | null | 978-1-4244-6896-6 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongying Zan | 1 | 15 | 19.05 |
| Kuizhong Kou | 2 | 0 | 0.34 |
| Jiale Tian | 3 | 0 | 0.34 |
| Ryan Sin | 4 | 0 | 0.34 |