## 1 Introduction  
With the growth of the Internet and the popularity of smartphones, an increasing number of individuals prefer to share their ideas on social networking sites [1]. A great deal of textual material is kept on the Internet, for instance, in online shopping platforms, such as Taobao and Jingdong, where people can express comments and opinions. The vast volume of social text information available on the Internet has a high commercial value. Mining this information can be a useful way for businesses to understand public attitudes toward their products, for celebrities to understand the emotions generated by their fans in response to their participation in public events, and for public institutions to understand Internet-based public opinion [2]. Sina Weibo, which means \"micro blog\" in Chinese, is a Chinese social networking platform similar to Twitter. Weibo are similar to Twitter tweets in that users can express their opinions by posting them. Super topics are a tag system for Weibo content. By adding a relevant super talk tag to a user's Weibo, the Weibo can be seen by more users who care about the topic; these tags also make it easier for users to quickly filter relevant content. The information in Weibo is also an important source of data for the social and natural sciences. Sina Weibo, a popular online social media platform in China, has become popular among Chinese netizens, with hundreds of millions of users using the Sina Weibo platform to keep up with the COVID-19 epidemic during the outbreak [3]. Users expect that complaints about products will be responded to in a timely manner. However, companies and users have a one-to-many relationship. Companies often receive huge amounts of information and are not able to see complaints from users in a timely manner. With automated sentiment analysis, it is possible to quickly look for negative comments in the messages. For companies, getting feedback from users can be an effective way to improve products and understand whether the design and service of the product is satisfying. For example, for a new product launch, social information about the product is collected on the Internet and, through sentiment analysis, labels such as surprised, angry, and fear are classified. Users are mostly satisfied with texts labelled 'like'; those labelled'surprised' highlight the features of the product; those labelled 'angry' and 'fear' often express the product's shortcomings. With this information, product sales strategies can be adjusted to provide ideas for the design of the next generation of products and to improve user satisfaction, thereby increasing the sales rate of the product. There are many small Internet businesses in China, which operate local businesses such as dating, brokerage, and renting [4]. These small local companies have few users, more local visibility than national visibility, and user communication intermingled with local languages as their major features. It is crucial for these businesses to remain up to date on consumer requirements and feedback. Although the field of natural language processing has accomplished a great deal with regard to text sentiment, these tiny enterprises do not have a large amount of data, and the human categorization of datasets is a substantial initial investment. If existing large datasets are employed, they run into the issue of local dialect features and the many types of sentiment analysis required by businesses. Predicting emotion based on a small sample size can assist small firms in resolving associated issues. Early machine learning techniques for sentiment analysis include support vector machines (SVM), naive Bayesian classifiers (NBC) [5], and classification and regression trees (CRT) [6].