# Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features
## 1 Introduction  
With the rapid development of the Internet, the public can participate in and discuss a wide range of topics on the Internet according to their background, positions and viewpoints. The development of online media has gone through three generations, including a one-way e-newspaper model represented by a portal, a social network and media model represented by Weibo and WeChat, and an intelligent model based on personalized recommendation information represented by Toutiao and Douyin. In the current internet ecology, different types of internet media complement each other, leading to the diversification of motivations, paths and modes of interaction in internet information dissemination and topic discussion. People can express their views on major social media platforms at any time, leading to a flood of content with a personal bias. This phenomenon of open comment, on the one hand, increases the motivation and initiative of public to participate in the management of society, on the other hand, it raises problems of the proliferation of fake news, the polarization of perceptions and the intensification of public opinion conflicts [1, 2]. The study of sentiment analysis has received extensive attention and research from interdisciplinary researchers, especially in the fields of fake news detection brought about by online media, social media bot/dong-army recognition, filter bubble identification and countermeasures [3, 4]. The most direct way to reflect public's attitude to these realnews events is to analyze the content of their published comments. The comment text on social media represents the views of each individual on the current issue, and accurate identification of the sentiment orientation in these comments can help observe and judge the value trend of the public towards hot events. However, comments in the online media were mainly short stories, which made the study difficult. Compared with ordinary texts, short Chinese web texts tend to have the following characteristics: Comments tend to be short in length, and most people do not form complex sentences when commenting. There is a lot of noisy data, and short text posted on the internet often contains misspellings due to the lack of strict writing requirements. There are many \"new words\" with special meanings, and social media platforms often have original words with special meanings. Traditional text recognition methods struggle to understand the sentiment orientation these words represent, and Chinese particles are ambiguous and polysemous. Because of the above characteristics, traditional text recognition models cannot accurately recognize the sentiment orientation of network short text. Research on recognizing sentiment orientation has accumulated to some extent, and most current methods rely on text content for identifying and judging sentiment orientation. These methods are based on natural language processing models to quantify Chinese words or characters [5; 6], and then map the entire sentence or paragraph to a vector space followed by the recognition of sentiment orientation and judgement via classification methods. While NLP methods are able to identify sentiment orientation information in text to some degree, existing models are not effective in these problems due to the semantic diversity of Chinese texts and the emergence of new words on the Web. In recent years, the rise of graph convolutional neural networks and the significant advantages of graph embedding algorithms have been accompanied by: Ability to handle large-scale graph data. Graph embedding algorithms typically have efficient computational complexity and can handle large-scale graph data with millions or billions of nodes. Ability to capture similarity and association between nodes. Graph embedding algorithms can map adjacent nodes to similar low-dimensional vector spaces, thereby preserving the similarity and association between nodes. Ability to support various application scenarios.