## 1 Introduction  
Sentiments can be reflected as the voluntary or involuntary reactions of human beings to the outside world when they conduct behaviors, such as talking, thinking, communicating, learning, and decision making. Since all these and similar behaviors can be expressed in sentences and paragraphs, sentiment analysis tasks have great significance to our daily life. In order to extract the features and behaviors of emotions, there are a variety of studies on sentiment analysis. Generally, negative sentiments affect people's bodies and minds, and positive sentiments make people more motivated in society and bring better, improving conditions [1]. However, sentiment analysis is a kind of abstract high-level pattern recognition without objective results, that makes such tasks challenging [2]. Moreover, there are often situations impacted by the current emotions, and the use of words and structures of the presented sentence also change accordingly. This kind of sequences makes the analysis process more difficult and time-consuming. For these reasons, a computer-based system with an effective neural network model is needed [3].  
Natural language processing (NLP) methods require a lot of feature extraction and expression of a sentence. Recurrent neural networks (RNNs) are commonly involved for NLP in the neural network approaches. In RNN-based NLP research, sentiment analysis is one of the major topics. The sentiment reflected from a paragraph can be considered through some features, including part-of-speech, the length of a sentence, and the content of a structure. [PERSON] et al. [4] was the first to apply a linear RNN for NLP tasks. However, this work reflects that each input feature has the same weight, and a feature often conflicts with the importance of various words. Since a word may have other meanings or a different importance level in various combinations, in particular, punctuations, prepositions, and postpositions are often less important than other words, the main information is almost always diluted if a linear RNN model is used. Therefore, deep learning-based approaches enable the learning of high-level features of the sentiment. An advanced RNN was proposed by [PERSON] et al. [5] to deal with multiple aspects within a single sentence.  
Furthermore, [PERSON] et al. [6] proposed a multilayer architecture that could obtain better accuracy on multiple benchmark datasets. In addition, [PERSON] et al. [7] extracted multiple aspects per sentence by using long short-term memory (LSTM) networks. Further, to exploit the relationship between part-of-speech and sentiment terms, a Content-Adaptive Recurrent Unit (CARU) was designed by [PERSON] et al. [8]. However, the above methods are targeted for the sentence-based input features, that perform poorly for the paragraph-based cases. Because all RNN models so far have only been able to consider the current and previous contents of a sequence, if we want to consider the trailing content to achieve a complete sentence understanding, we must find a new network model, which is beyond the capabilities of the RNN units.  
In this article, we propose a hybrid neural network model stacking a multilayer CARU structure in the flow with the Chebyshev pooling in order to obtain the probability distribution of features for a better interpretability. We present the architecture and implementation in full detail, and provide the experiments to compare with the state-of-the-art approaches to sentiment analysis. The rest of this article is outlined as follows: Section 2 reviews the neural approaches to NLP and sentiment analysis tasks, together with their application. Section 3 first summarizes the overall architecture of our integration and the major contribution, then presents the details of the key components. We provide our concrete implementation of the network in Section 4 and discuss the experimental results in Section 5. Finally, Section 6 concludes the work.