Abstract: In this paper,we used the machine learning method to classify the sentiment classification of Chinese product reviews.The method combines SVMperf and word2vec.Word2vec trains out each word of the corpus of word vectors.By computing the cosine distance between each other,a similar concept word clustering is achieved,and with similar feature clustering, the vocabulary of the high similarity in the field is expanded to sentiment lexicon.The high dimensional representation of the word vector is trained out using word2vec.PCA principal component analysis method is used to reduce the dimension of the high dimensional vector,and the feature vector is formed.We used two different method to extract the effective affective feature,which is trained and predicted by SVMperf,so as to complete the sentiment classification of the text.The experimental results show that the method can obtain good results,regardless using the similar concept clustering method to expand the task or complete the emotional classification task.

Key words: Sentiment classification,Word2vec,SVMperf,Semantic features,PCA