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

In this article, how word embeddings can be used as features in Chinese sentiment classi-fication is presented. Firstly, a Chinese opin-ion 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, includ-ing SVM, Logistic Regression, Convolutional Neural Network (CNN) and ensemble meth-ods. 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 out-standing 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.