Research on text sentiment classification based on improved feature selection method - Mingxin LIU; Jing CHEN; Qiyuan WANG (Dianxin kexue)
An improved information gain feature selection method based on sentiment dictionary was proposed.Firstly,aiming at the existing problems of information gain feature selection,such as paying attention to the frequency of feature word and ignoring the balance of corpus,an improved method was proposed.Secondly,considering the influence of sentiment words in text classification,a feature selection method IGSC (information gain combining sentiment classification) based on sentiment dictionary was proposed for text classification.By matching the text emotion words and combining the weight of emotion words,the feature dimension reduction was realized and the problem of text data sparseness affecting classification performance was solved.Finally,according to the proposed feature selection method of travel review data set for experimental verification and analysis,the experimental results show that the improved text sentiment classification feature selection method has been improved in terms of classification accuracy,recall and F value,and classification has better stability.