CRF Based on LHFS Applied on Sentiment Classification

Author

Zhu, Jian

Author_Institution

China Youth Univ. For political Sci., Beijing, China

Volume

1

fYear

2012

fDate

23-25 March 2012

Firstpage

217

Lastpage

219

Abstract

Sentiment classification has attracted increasing interest from natural language processing. This paper applies CRF(Conditional Random Filed) based on LHFS (Local High-Frequency Strings) method on sentence sentiment analysis. This method can effectively solve ordinal regression problems. In this method, sentences are labeled to determine their polarity, and LHFS method is used to expand the set of sentiment features. Experiments on sentiment classification indicate that the accuracy of CRF model is increased up to 2.1%, with the help of LHFS method, which is much better than that of HMM and MEMM.

Keywords

natural language processing; pattern classification; regression analysis; text analysis; CRF; LHFS; conditional random field; local high-frequency strings method; natural language processing; ordinal regression problems; sentence sentiment analysis; sentiment classification; Accuracy; Classification algorithms; Data models; Hidden Markov models; Motion pictures; Support vector machines; Training; conditional random filed; feature selection; opinion extraction; sentiment classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-0689-8

Type

conf

DOI

10.1109/ICCSEE.2012.190

Filename

6187807