## 1 Introduction  
In recent years, Internet social media platforms have emerged rapidly, and because of their advantages of rapid dissemination, wide audience, and ease of use, more and more Internet users are choosing to use Internet social platforms to express their opinions and exchange information, resulting in a huge amount of text data with emotional overtones. In particular, Microblog, as the world's largest Chinese social platform, has become an important way for the public to express their emotions, opinions, and attitudes [1]. According to Microblog's Q4 2021 earnings report, Microblog reached 573 million monthly active users by the end of Q4 2021, up 10% year-on-year, and 249 million daily active users, up 11% year-on-year. Due to the popularity of Microblog and the low threshold for posting information, the discourse on this social media platform provides the most up-to-date and extensive information. Therefore, sentiment analysis based on microblogs has always been a hot topic for research. By analyzing the sentiment of microblogs and mining valuable information, we can obtain information about people's preferences for specific products, and their concerns and emotional changes regarding government policies or social phenomena. This is important for enterprise product marketing, online opinion monitoring and analysis, national and social security, etc. [2]. However, there are significant differences between microblog sentiment texts and ordinary texts: microblog sentiment texts have a large number of \"different meanings for the same word\"; they are more colloquial and contain many Internet buzzwords with a higher degree of irregularity; and they are shorter and contain limited information, which requires a higher level of ability in semantic understanding within the model. All these present considerable challenges in microblog sentiment analysis [3]. Lexicon and traditional machine learning are the main approaches used in early microblog sentiment analysis studies. Although lexicon and rule-based methods are simple, easy to implement, and perform rapid classification, their accuracy depends on the quality of the sentiment lexicon and the rationality of the manually designed rules. It requires extensive manual intervention; thus, the actual classification effect is not ideal [4]. Sentiment analysis methods based on traditional machine learning need to extract feature information from the text and then use classification algorithms to classify the sentiment polarity of the text, generally using three classification algorithms: Naive Bayes (NB), Support Vector Machine (SVM), and Maximum Entropy (ME) [5]. Although the effect of these methods is better than when using lexicon-based sentiment analysis methods, their generalization ability is poor and their accuracy decreases rapidly with increasing data volume and the emergence of various new language scenarios. In recent years, the application of deep learning models to solve sentiment analysis problems has gradually become the norm with the rapid development of deep learning [6]. By training deep learning networks and automatically adjusting their weighting parameters, large-scale data can be processed efficiently with superior accuracy and time and effort savings. The task of text sentiment analysis can be efficiently accomplished using deep learning networks. The advantages of using deep learning models for the sentiment analysis of microblogs are obvious when compared with earlier sentiment analysis methods. However, due to the characteristics of microblog texts, which require models with a high level of semantic understanding, there are still some shortcomings in existing models with respect to accomplishing the task of sentiment analysis in microblogs. First, the accuracy of the text coding of most existing models needs to be improved. Most of the commonly used text representation methods focus only on character-level or word-level features.