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| Feature Selection for Cross-Domain Sentiment Classification |
| ZHANG Yu-Hong,ZHOU Quan,HU Xue-Gang |
| School of Computer and Information,Hefei University of Technology,Hefei 230009 |
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Abstract The data is usually unlabeled in application,which makes the adaptation of cross-domain effective. However,the sentiment classification is domain-dependent. The feature space of source domain,gotten by feature selection,can not represent the common character of both domains and is not suitable for the classification of target domain. Therefore,an approach of feature selection for cross-domain sentiment classification,Log-Likelihood Ratio-Term Frequency (LLRTF) is proposed. The log likelihood ratios (LLR) of features are computed in source domain,by which the discriminative feature space is gotten. Then,the statistic information term frequency of both domains is added to the LLR,and the features which are more important in target domain are selected. The feature space construction based on the LLRTF reduces the difference between source domain and target domain. The experimental result shows that the LLRTF is superior to the baselines.
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Received: 05 February 2013
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2013, Vol. 26 
