# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 5 Conclusions and Future Outlook  
In this paper, we propose an aspect-level text sentiment analysis method based on a convolutional neural network model and a bidirectional threshold recurrent neural network. By cleaning 5G crawler text, Chinese word separation, deleting meaningless discontinued words, and filtering lexicality, the original dirty data were turned into a corpus that could be directly input into the model; in the aspect ontology extraction session, the TF-IDF algorithm was used to obtain feature word vector weights; in the model building session, the one-hot word embedding technique was used to vectorize the subtext, CNN extracted local feature information and GRU extracted the long-distance dependency information, and both below and above information were processed through a bidirectional structure. Based on this, this study used CNN + BiGRU to obtain the contextual information of the text, managing to fully extract the local features of the review text, improve the accuracy of sentiment classification, avoid the gradient explosion and gradient disappearance problems, and reduce the loss value of the model by constantly adjusting the experimental parameter settings. Finally, by analyzing the accuracy, recall, and F1 score metrics, the CNN + BiGRU model constructed in this paper was determined to have significantly improved in each metric compared with the CNN model, LSTM model, and C-LSTM model. In the comparison with the most basic CNN network model, the CNN + BiGRU model in this paper improved the accuracy by 12%; compared with the LSTM model and C-LSTM model, the CNN + BiGRU model in this paper improved the accuracy by 8% and 4%, respectively, and the model in this paper not only improved the classification effect and achieves the optimum result in all criteria, but also reduced the operation time significantly. It can be seen that the model in this paper has some applicable advantages in aspect-based sentiment analysis research of short comment texts, which can provide reference for the future direction of sentiment analysis. Aspect-level sentiment analysis can extract all the sentiment tendencies expressed by consumers in all aspects of a product, and merchants can formulate targeted policies on this basis. Applying aspect-level sentiment analysis to recommendation systems can improve their practicability. Therefore, aspect-level sentiment analysis has high research value. This paper constructed a CNN + BiGRU model for aspect-level sentiment analysis, which improved relevant indicators to a certain extent. Regarding aspect-level sentiment analysis, there are some other different and valuable research works; [PERSON] [32] used graph neural networks to capture the implicit features between nodes in sentence relation graphs for sentiment analysis. Considering the problem of processing graph-structured data, [PERSON] et al. [33] proposed a sentiment analysis model based on distance and graph-structured convolutional neural networks. The authors' academic ability, research time, and academic energy are limited. Future studies could conduct in-depth research in the following areas:  
* In the sentiment classification section, this paper only divided sentiments into positive and negative ones. Further research could carry out specific grading calculations for certain emotions, and further refine the classification of words of praise and derogation. * In specific short-text contexts, different emotional entities are usually given different degrees of importance. Therefore, further research could introduce an attention mechanism to help exclude useless information and find key information from big data quickly. * The CNN + BiGRU model could be used for feature fusion and applied to other fields, such as the technology for text backdoor attacks [34], mode recognition [35], etc. * Similar models have been applied in sentiment analysis in other languages. [PERSON] et al.