## 1 Introduction  
With the exponential growth of large collections of opinion-rich resources, sentiment classification [1] has been one of the most important tasks in natural language processing (NLP), which aims to automatically classify the sentiment polarity of a given text as negative, positive or more fine-grained classes. It can help companies to process and extract precious data from massive amounts of information, which contain great business value in brand monitoring, customer services, market research, politics, and social services. For example, tracking consumers' overall appreciation of a certain product can assist merchants in adjusting their marketing strategy. A fundamental problem of NLP is text representation learning, which is encoding the text into continuous vectors by constructing a projection from semantics to points in high-dimensional spaces. The effect of text sentiment classification mainly depends on extracting compact and informative features from unstructured text through representation learning. Mainstream representation models for text sentiment classification can be divided into two categories based on the knowledge and information they use: traditional machine learning-based representation models and current popular deep learning-based representation models. The traditional machine learning-based representation models train a sentiment classifier relying on sentiment linguistic knowledge such as bag-of-words and sentiment lexicon, where the sentiment polarity of text is largely determined to be positive if the number of positive words is larger than that of the negative ones. In contrast, the current popular deep learning-based representation models utilize the deep neural network to learn the semantic information contained in text. The performance of deep learning-based representation models is often more superior than that of machine learning-based representation models when the syntactic structure of text is complex. In this paper, we mainly focus on integrating sentiment linguistic knowledge into the deep neural network to enhance the quality of text representation learning for sentiment classification task. The main idea of the traditional machine learning-based representation models [2] is to employ text classifiers such as the naive Bayes classifier, maximum entropy model, and support vector machines to predict the sentiment polarity of the given texts. The performance of the naive Bayes classifier for text sentiment classification is based on the conditional probability of word-level features belonging to certain sentiment class, where features are manually designed by the bag-of-words representation method [3]. The maximum entropy model is also a statistical machine learning method based on the bag-of-words representation approach and its accuracy of sentiment classification entirely depends on the quality of the hand-crafted corpus so that the process of parameter optimization is computationally intensive and time-consuming [4]. The support vector machines constructs the sentiment feature vector mainly relying on the frequency of occurrence of words in text and trains the decision function to predict the sentiment polarity of sentences [5]. The success of these machine learning algorithms generally relies on improving feature engineering work in terms of the bag-of-words representation learning method and manual sentiment lexicon. For example, considering each sentence as a vector with the following groups of features: n-grams, character n-grams, non-contiguous n-grams, POS tags, cluster n-grams, and lexicon features, support vector machines [6] can beat all strong competitors to be the best performer of traditional machine learning models in the SemEval-2014 task. However, traditional statistical representation learning-based feature engineering work is labor intensive, and it is difficult to breakthrough its performance bottleneck for text sentiment classification tasks in the current field of NLP due to the failure to encode word order and syntactic information.