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In this article, how word embeddings can be used as features in Chinesesentiment classification is presented. Firstly, a Chinese opinion corpus isbuilt with a million comments from hotel review websites. Then the wordembeddings which represent each comment are used as input in different machinelearning methods for sentiment classification, including SVM, LogisticRegression, Convolutional Neural Network (CNN) and ensemble methods. Thesemethods get better performance compared with N-gram models using Naive Bayes(NB) and Maximum Entropy (ME). Finally, a combination of machine learningmethods is proposed which presents an outstanding performance in precision,recall and F1 score. After selecting the most useful methods to construct thecombinational model and testing over the corpus, the final F1 score is 0.920.

An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding