基于混合互信息算法的文本情感分析

iming at the phenomenon of positive and negative correlation in the feature selection method of mutual information (MI) and the problem of not considering the word frequency of the feature items in different categories, a hybrid mutual information feature selection algorithm (HMI) is proposed. By introducing the inverse document frequency coefficient and the inter-class word frequency information coefficient, the algorithm can effectively utilize the word frequency information in the whole document and the word frequency information between each class. The positive and negative correlation coefficient is introduced to distinguish positive correlation and negative correlation and to make effective use. The experimental results show that the hybrid mutual information algorithm can effectively improve the quality of feature selection and then improve the effect of text emotional analysis.