# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 1 Introduction  
With the development of e-commerce, more and more people are becoming willing to post their opinions and comments on certain products after consumption, forming a large number of comment texts. These short texts generally have a strong subjective nature, and sometimes contain different emotional tendencies within a sentence. Additionally, short text comments are highly colloquial. This makes the topic of the text vague and difficult to find, semantically incoherent, and, more disturbingly, difficult for researchers to use directly. At the same time, most current research on the sentiment analysis of short comment texts involves analysis from a coarse-grained perspective--that is, only one sentiment tendency is obtained from a paragraph. However, current user reviews generally contain a variety of complex emotions. For example, a product comment in Chinese read as follows: \"I love this nice new look of the phone and its glass case, which makes it look fashionable. But it may be lagging after two week\". In a coarse-grained sentiment analysis, this comment would be marked as simply positive. In fact, the user only gave favorable comments on the appearance of the product, and negative comments on the product quality. It can be seen that coarse-grained sentiment analysis cannot accurately reflect the specific aspects that users really care about. Aspect-based sentiment analysis can determine the sentimental tendencies of different aspects in certain product review ontologies. Based on this advantage, aspect-based sentiment analysis of short texts of user reviews can better help consumers to make judgments and decisions, and can also help businesses to make targeted product improvements and increase user satisfaction.  
In response to the above problems, this aim of this study was to improve the accuracy of aspect-based sentiment analysis through deep learning research methods. Convolutional neural network (CNN) can extract local feature information in a Chinese comment corpus very well, but can easily miss the long-distance features of the text in the extraction process. Recurrent neural network (RNN) has good memory ability and it is often used to extract the long-distance dependent information of a review corpus, as it can make up for the shortcomings of the convolutional neural network. At the same time, the bidirectional gating recurrent unit (BiGRU) is an \"upgraded\" version of the RNN, which can better solve the problems of gradient explosion and gradient disappearance. Based on the complementary advantages of the above two neural networks, this paper describes the construction of a hybrid model based on CNN and BiGRU, referred to simply as the CNN + BiGRU model. This model can extract the required local feature information and capture the aspect level of comments. Additionally, it can avoid the problems of gradient explosion and gradient disappearance, improve the accuracy of the model, and reduce the computational overhead. At the same time, in practical application, this model can also be used in the emotional analysis of short texts such as microblog comments and social public opinion posts, which play a guiding role in relevant fields of all walks of life.