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