Automatic Chinese sentiment word extraction based on Aximum Entropy

Author

Li, Si ; He, Hui ; Xu, Wei-ran ; Guo, Jun

Author_Institution

Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2009

fDate

12-15 July 2009

Firstpage

437

Lastpage

441

Abstract

In recent years, sentiment analysis has become a hot topic in the study of natural language processing. Methods of machine learning are widely used to the sentiment analysis. This paper presents an approach for Chinese sentiment analysis at phrase-level. A LMR template is designed to tag word features, like position, orientation, part of speech (POS), and so on. Then, maximum entropy (ME) model is employed to extract sentiment words. Parts of the first Chinese Opinion Analysis Evaluation (COAE2008) corpus are used in evaluation. Experimental results show that ME model with LMR template can achieve a good performance.

Keywords

learning (artificial intelligence); natural language processing; word processing; Chinese Opinion Analysis Evaluation corpus; LMR template; automatic Chinese sentiment word extraction; machine learning; maximum entropy; natural language processing; sentiment analysis; Cameras; Data mining; Entropy; Information analysis; Internet; Land mobile radio; Natural language processing; Pattern analysis; Pattern recognition; Wavelet analysis; LMR template; Maximum entropy; Sentiment word;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3728-3

Electronic_ISBN

978-1-4244-3729-0

Type

conf

DOI

10.1109/ICWAPR.2009.5207489

Filename

5207489