## 2 Related Work  
Current reports on sentiment classification can be roughly divided into three parts: sentiment-knowledge-based [11], conventional machine learning-based [12], and deep learning-based [1]. The knowledge-based approach utilizes the sentiment lexicon, rules, and patterns. Its accuracy depends on the maturity of the lexicons. Machine learning approaches normally use the bag of words, part of speech, and so on, as features to train the mathematical models. The most commonly used conventional machine learning models include support vector machine (SVM), naive Bayes (NB), and maximum entropy (ME). Deep learning approaches usually extract text features automatically and train classifiers through deep learning models such as convolutional neural network (CNN) and recurrent neural network (RNN). Sentiment-knowledge-based approaches use universal or domain-specific dictionaries to obtain emotion. [PERSON] et al. [13] built an extended sentiment dictionary including basic sentiment words, field sentiment words, and polysemic sentiment words. Their approach is effective for sentiment recognition in Chinese comment texts. [PERSON] et al. [14] built a large-scale sentiment lexicon by a representation approach and treated sentiment lexicon learning as a phrase-level sentiment classification. [PERSON] et al. [15] built adaptive sentiment lexicons to improve the accuracy of polarity classification in microblogs. The approaches based on machine learning demand a number of meaningful well-adopted features as input, and then uses various classifiers to determine the sentiment polarity. [PERSON] et al. [12] applied machine learning to sentiment classification for the first time, tried two text features of \(n\)-gram and part of speech, and found that SVM had the best accuracy. [PERSON] et al. [16] proposed a hierarchical structure-based hybrid approach for sentiment analysis and analyzed the contribution of various features in SVM. [PERSON] et al. [17] presented a semisupervised and user-level framework to predict the sentiment polarity by utilizing information about user-user relationships in social media. [PERSON] et al. [18] proposed recursive deep models to compute compositional representations for variable length and syntactic type phrases. Those models are employed as features to classify each phrase. Approaches based on deep learning usually learn from large-scale labeled data to high-level feature representations of text through a multilayered neural network. [PERSON] et al. [19] used a CNN to incorporate much more parameters to accurately classify the sentiment polarity of movie reviews. However, CNNs struggle to capture the context in a piece of a message, particularly for a Chinese microblog, which includes much more information than an English counterpart. [PERSON] et al. [20] applied a long short-term memory (LSTM) network for twitter sentiment prediction. This type of deep neural network-based model employs gated units to select appropriate parameters for time series. [PERSON] et al. proposed a HHAS model to improve the accuracy of aspect-level sentiment analysis [21]. The above methods usually demand large amounts of well-labeled data. To some extent, the performance of deep learning methods depends on the quality of the annotated corpus. For example, at least 10,000 pieces of messages are required to train the deep neural networks. Unfortunately, domain-specific datasets cannot be obtained easily. Moreover, because social media contain a large range of topics, labeling the data manually will inevitably incur errors [22]. This problem has attracted the attention of researchers in recent years. Semisupervised classification significantly reduces the demand of labeled data. The variational autoencoder (VAE) has been widely used to automatically annotate unlabeled data. However, this approach is immature and cannot be directly used in practice [23].