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.

本文介绍了如何在中文情感分类中使用单词嵌入作为特征，并建立了一个包含一百万个评论的中文意见语料库。通过使用不同的机器学习方法，包括支持向量机 (SVM)，逻辑回归 (Logistic Regression)，卷积神经网络 (CNN) 和集成方法，这些方法相对于使用朴素贝叶斯 (NB) 和最大熵 (ME) 的 n-gram 模型获得了更好的性能。最后，提出了一种组合机器学习方法，并在语料库上进行测试，最终的 F1 分数为 0.920。