# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 2 Literature Review
[12] established a fine-grained feature extraction model based on BiGRU and attention, and proved through experiments the role of the BiGRU model in improving the accuracy of sentiment analysis. Chinese text sentiment analysis is a process of analyzing sentences and judging the subjective feelings, opinions, and attitudes of the sentence users through a series of methods. Deep learning has been widely used in the field of text sentiment analysis. Traditional deep networks such as RNN and LSTM have been applied by many scholars in aspect sentiment analysis [13]. The function of sentiment analysis technology is to judge the emotional tendencies of Chinese sentences, judge whether the reviewer is positive or negative, and divide the text into several categories according to the reviewer's attitude. According to the delicacy and emphasis of the evaluated content, it can be divided into three levels: discourse level, sentence level, and aspect level. [PERSON] et al. [14] combined a multi-hop inference network to transform a sentiment analysis task into a reading comprehension task, and proposed a text sentiment analysis model based on multi-hop inference. Furthermore, scholars have refined the content of the research objects to study the sentiment analysis of sentence-level text. [PERSON] et al. [15] proposed an algorithm based on the contribution of emotional polarity which predicts the sentence polarity of a corpus based on the position of words in the sentence, and proved the effectiveness of the algorithm through experiments. At present, the discourse-level and sentence-level sentiment analysis methods and technologies have been developed to a level of relative maturity, but both of these groups focus on overall sentiment analysis. This can lead to omission of details and miscalculations in application. Aspect-level sentiment analysis technology can be used to discover different objects in an aspect and to identify the emotional information expressed in a text for each aspect, effectively solving the appeal problem. Aspect sentiment analysis was only proposed in 2010, and there have been few studies on this topic so far. It consists of two subtasks: aspect item extraction and aspect sentiment classification [16]. Specifically, aspect item extraction aims to extract the attributes of goods or services from a comment text; aspect-level sentiment classification should judge the emotional tendency corresponding to each aspect. In aspect extraction, [PERSON] et al. [17] solved aspect extraction as a sequence labeling problem, and then used a linear chained conditional random field to deal with this problem. Traditional methods (such as constructing sentiment dictionaries) completely separate text representation from feature extraction and model training, and focus on text representation and feature extraction. Due to the randomness, high ambiguity, irregularity, and other characteristics of short text, this can easily lead to the problems of feature dispersion and context independence in the process of text representation and feature extraction. All of these factors may lead to lower accuracy of feature extraction and disconnection of contextual semantic relations when using traditional sentiment analysis methods [18]. To improve the accuracy of sentiment analysis based on sentiment lexicographs, [PERSON] et al. [19] proposed a time-varying sentiment lexicographs based on incremental word vectors that train an incremental word sentiment classifier from a dynamic word vector to automatically update the sentiment lexicographs. In aspect-based sentiment classification, there are multiple emotions co-existing in the short-text aspect-based sentiment analysis. Although this problem can be solved by refining the types of sentiment labels, this approach may also lead to a problem in that the model is too complex and the essence of the problem is not solved [20]. If the number of network layers in RNN is too great, the problem of gradient explosion or gradient disappearance will occur [21].