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.
@cite_0: Web content mining is intended to help people to discover valuable information from large amount of unstructured data on the Web. Sentiment classification aims to mining the Web content of product reviews by classifying the reviews into positive or negative opinions. Such kind of classification approaches could help both consumers and sellers in making their decisions. But it is also a complicated task with great challenge. This paper conducted a comparison between the SVM approach and semantic approach for sentiment classification of Chinese reviews and also proposed some improvement for sentiment classification approaches. Experimental result indicated that, compared with previous researches for English reviews, the performance of both approaches for Chinese reviews sentiment classification are acceptable, while the support vector machine approach has better performance than the semantic orientation approach.
However, compared to English sentiment analysis, there are relatively few investigations conducted on Chinese sentiment classification until 2005 @cite_0 . presented a study on comparison of different machine learning approaches under different text representation schemes and feature weighting schemes. They found that SVM achieved the best performance. After that, found 6,000 or bigger for the size of features would be sufficient for Chinese sentiment analysis, and sentiment classifiers were severely dependent on domains or topics.