# Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism
## 1 Introduction  
Accompanying the continuous development of social networks, the role of Internet users also has changed quietly from the original recipient of information to the creator of information. More and more people are expressing their opinions through the Internet and, gradually, form a short text-based expression. The text information has a large amount of data, and the text content is scattered and disorganized. It is difficult to distinguish. Therefore, how to use natural language processing related techniques to analyze the emotional polarity of short texts in social networks has become one of the hotspots of current research [1; 2]. Deep learning is an important branch of machine learning. Deep learning is an algorithm that uses high-order abstraction of multiple nonlinear transformation structures [3]. Recently, more and more researchers have begun to use deep learning technology in the field of image and speech recognition to solve the problem of text sentiment classification. [PERSON] [4] proposed an English classification model, taking preprocessed word vectors as input and using convolutional neural networks (CNN) to achieve sentence-level classification tasks. [PERSON] et al. [5] used CNN to solve the polarity judgment problem of twitter. Although CNN has made great breakthroughs in the field of text categorization, it pays more attention to local features and ignores the contextual meaning of words, thus affecting the accuracy of classification. Therefore, improvements tothe CNN model have been ongoing. [PERSON] et al. [6; 7] proposed capsule networks and dynamic routing based on convolutional neural networks and achieved better classification results than traditional CNN. [PERSON] et al. proposed applying the RNN model to text classification tasks [8]. Since the output value of the current node of the RNN is determined by the current input and output of the previous node, the word structure of the text is considered, but the RNN is prone to problems such as gradient dispersion. Although long short-term memory (LSTM) can solve the gradient dispersion problem existing in RNN, LSTM has a large computational complexity due to its structural complexity and stores a large number of redundant intermediate variables. Therefore, a large amount of training time and memory space is required, and it is overly dependent on historical information and cannot utilize future information. Bidirectional gated recurrent unit (BiGRU) is a variant of bidirectionally long short-term memory (BiLSTM) and GRU. BiGRU structurally streamlines the BiLSTM three-door (forget, input, output) structure into two gates, namely update and reset, so fewer parameters accelerate the convergence of the model. Simultaneously, BiGRU combines the characteristics of short text content with high context, fully considers the meaning of words in context, and overcomes the problem of semantic information after the original GRU cannot consider words. The attention mechanism was first applied in the field of image processing. [PERSON] [9], in 2014, proposed to use the attention mechanism in the image classification task and achieved good experimental results. [PERSON] [10], in 2015, (and others) proposed one based on multi-layer attention. The convolutional neural network of the force mechanism is applied to the sentence modeling to better capture the local text features and verify the effectiveness of the combination of the attention mechanism and the convolutional neural network. Aspect-based sentiment analysis (ABSA), as an important sub-task of sentiment analysis, is a deeper sentiment analysis with the goal of identifying emotional polarity in different aspect contexts (positive, neutral, negative). Given a sentence \"The pizza in this restaurant is very delicious, but the quality of the takeaway pasta is very poor.\", for example, the emotional polarity of the term \"pasta\" is negative, while the aspect is of \"out of the box\". The emotional polarity is opposite to the emotional polarity of \"pizza\".