# Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features
## 1 Introduction
Graph embedding algorithms can be used in social network analysis, recommendation systems, bioinformatics, and other fields. Graph embedding algorithms have gradually been applied in the field of sentiment orientation classification [6; 7]. This paper thus design a model based on word vectors and methods for embedding heterogeneous network graphs for comment response structures and textual representations in news stories. Using NLP methods, the model converts short text comments to word vectors and combines the feature vectors of neighbouring comments in the current comment domain (the neighboring comments include the first-order neighborhood and higher-order neighborhood range of neighboring nodes). Attention mechanisms can weight different parts of input, allowing the model to pay closer attention to important information and better understand the context of the input. Therefore, our model incorporates an attention mechanism for extracting important features and ultimately uses a classifier to classify the sentiment orientation of comments. Given the paucity of datasets containing feedback-reply relationships, 511 public news items about Huawei and corresponding comments were obtained from Today's Headline media platform as the dataset, covering a range of hot topics such as finance and technology. All comments in this article have been manually annotated for sentiment orientation and performed some exploratory research on sentiment analysis of user comments on this dataset, with the following key contributions:  
1. Given the special nature of Chinese short texts, a method of extracting features from the comment structure to modify text vectors was adopted in order to achieve more accurate vector mappings. To improve the classification accuracy of sentiment orientation for Chinese short texts, we combined the dynamic comment representation vectors generated by the text vectorization model with features of the comment network structure. 2. In response to the heterogeneity of the comment structure network (the sentiment orientation expressed by comments with reply relationships is often opposite), in this paper, we proposed the HGAT model for efficient feature fusion, and we also incorporated an attention mechanism to aid in feature embedding. The proposed model can further improve text features, and even with a relatively simple classifier, it can obtain good prediction results for the sentiment orientation of the comments. 3. For the proposed HGAT model, a Toutiao dataset is proposed in this paper, and it is verified on the Toutiao dataset that the model in this paper performs better on the dataset of Toutiao compared to the text-only classification method and the graph embedding method based on isomorphism network. The remainder of this paper is organized as follows. Section 2 is devoted to related work, including opinion tendency recognition, opinion conflict detection, and graph embedding algorithms. Section 3 details the proposed opinion tendency recognition method based on heterogeneous network graph embedding fused attention mechanism in this paper, where the related designs of word embedding layer, input layer, coding layer and output layer are explicitly given. Section 4 conducts experiments on the Toutiao dataset based on the method proposed in this paper, and shows the comparison experimental results between this method and NLP-based, structure-based methods and combined NLP and structure-based algorithms, through which it is verified that the model in this paper has better classification effect and better stability. Section 5 summarizes the various advantages of our proposed model and points out some directions for future work.