数据标记的难以获取使得跨领域适应成为一种有效的途径。然而情感分类具有较强的领域依赖性,利用传统的特征选择方法在原始领域构建的特征空间不能体现领域间的共性,难以适用于目标领域。为此,提出一种面向跨领域情感分类的特征选择方法( LLRTF),利用对数似然比选取在原始领域富有判别力的特征,并通过对照两个领域的统计信息,选出其中在目标领域影响较大的特征。基于该方法构建的公共特征空间,能减少领域间数据分布的差异。实验结果表明,LLRTF优于基准算法。%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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