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Sentiment Classification Using Supervised and Semi-supervised Conditional Maximum Entropy Modeling
Qi Zhang * #,Xuangjing Huang,Lide Wu
School of Computer Science, Fudan University
*Correspondence author
#Submitted by
Subject:
Funding: Chinese NSF，Doctoral Fund of Ministry of Education of Chinaand Science，Shanghai Science and Technology Development Funds （No.60673038，200802460066，08511500302）
Opened online:25 March 2009
Accepted by: none
Citation: Qi Zhang,Xuangjing Huang,Lide Wu.Sentiment Classification Using Supervised and Semi-supervised Conditional Maximum Entropy Modeling[OL]. [25 March 2009] http://en.paper.edu.cn/en_releasepaper/content/30713
 
 
This paper presents our work on determining the sentiment polarity of sentences and articles. Feature combinations is the first novelty of our approaches. In order to explore better feature sets, we do some data analysis of the MPQA corpus and a few of experiments to evaluate combinations. Through those efforts, our approach achieves 77.0% accuracy in MPQA data set. Another novelty of this work is that Semi-supervised Conditional Maximum Entropy (SCME) modeling is used to combine labeled and unlabeled data. Experimental results show that our approach can significantly improve performance. In sentence-level, our approach achieves 78.2% accuracy in MPQA data set, the relative improvement given by semi-supervised technique is 5.2% over the supervised method.
Keywords:Semi-Supervised Learning; Maximum Entropy; Sentiment Classification
 
 
 

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