基于扩展的情感词典和卡方模型的中文情感特征选择方法

Chinese emotion feature selection method based on the extended emotional dictionary and the chi-square model

摘要:

根据经典的特征选择方法在中文情感评论文本中应用的缺陷和不足，提出了一种改进的中文情感特征选择方法。目前，现有的情感特征选择方法普遍只利用了特征项在褒贬类中的统计信息，忽略了情感极性值对特征选择的影响；同时情感文本中否定词会带来特征项情感极性反转的情况，为特征选择带来较大的负面影响。针对这些问题，首先对情感文本中的否定词进行了检测和判定，对否定词界定范围内的情感特征词进行反义变换处理，有效的解决了情感文本中极性反转的问题。同时还将特征项的情感极性值和其在类中的频率特点两个因素融入到卡方特征选择模型(CHI)中，从而提升了卡方模型在文本情感特征选择的效果。实验结果表明，本文算法较其他算法在多个领域数据集上的情感分类准确率提高了1.5%左右。

Abstract:

According to the defects and deficiencies of the classical feature selection method in Chinese comment text, an improved method is proposed for Chinese emotion feature selection. The current existing emotion feature selection method generally only used the statistical information of the feature items in the classes, ignoring the influence of the emotional polarity value on feature selection. Meanwhile the negative words in the sentiment text can cause the reversal of the emotional polarity of the characteristics, which would bring great negative effects on the feature selection. To tackle these problems, antisense transformation processing of the emotion characteristic words is performed in the range of the negative words, which effectively solves the emotional polarity reverasl in the sentiment text. The paper also introduces the emotional polarity values and its frequency into the chisquare model (CHI) to improve the effect of CHI on the emotion feature selection. The experimental results show that the proposed method can improve the accuracy of emotion classification by about 1.5% in multiply domain data sets, compared with other algorithms.