# Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features
## 2 Related Work
### Graph Embedding  
Graph embedding methods are now a common approach to network analysis and research. Graph embedding methods map nodes, edges or the entire network of graphs into a low dimensional vector space for representation, and then use machine learning, deep learning, and other methods for downstream tasks such as classification of nodes, prediction of links, and classification of graphs.  
DeepWalk [26] is considered to be the first work in this area in graph embedding problems. DeepWalk collects a series of sequences of nodes from the graph via random walks and then vectorizes each representation of the nodes using the Skip-Gram [27] model. The node2 vec [28] method expands the search space of DeepWalk's sampling strategy by combining breadth-first search and depth-first search in order to obtain more global and local structural features in a better way, which is referred to as biased random walks, improving the representational learning capability of the network. The development of this typical research field has been accelerated by the emergence of deep learning methods. Variants of Graph Neural Networks (GNN) include GCN (Graph Convolution Network) [29], which provides a simplified method for computing graph embeddings. Based on the structure of the CNN model, the GCN model captures the graph structure and corresponding feature information of each node through graph convolution calculation, and converts the structural information between nodes into vector representations.  
Finally, the emergence of attention mechanisms provides a novel method for making accurate predictions based on the weighted combination of all encoded input feature vectors. [PERSON] et al. [30] proposed an alternative method to utilize attention mechanisms in dynamic heterogeneous networks. The system employs three types of attention: structural, semantic and temporal, and obtains a better performance.  
All of the aforementioned graph embedding methods based on message passing and attention mechanisms are based on the assumption of network isomorphism, i.e., nodes in the network are primarily connected with nodes belonging to the same attributes or classes. Real world networks, however, do not always satisfy the isomorphism hypothesis. To address highly heterogeneous networks, [PERSON] et al. [31] proposed the H2 GCN model, which aggregates neighborhood node characteristics and uses node degree values for normalization to update node characteristics, thus improving the efficiency of graph embedding algorithms in heterogeneous networks. [PERSON] et al. [32] proposed a method to improve the performance of cross domain classification tasks using network embedding similarity metrics.  
With respect to the application of graph embedding algorithms in sentiment analysis of opinions, [PERSON] et al. [7] used a dependency tree to build simple syntactic dependency relations and used a graph convolutional neural network to fuse the syntactic information, learn text-to-word vector representations, and derive the final vector representation weighted by the importance of the context content. The algorithm introduces graph embedding methods to sentiment classification, but there are few methods that combine feedback response relationships with feedback text for exploration and search.  
To summarize, feature-based algorithms of text or network structure have achieved some effects in sentiment classification problems. For text vectorization methods, on the other hand, they ignore the structural information of the feedback response context in social media, although pure network methods fall short for information mining of textual information. Motivated by the above situation, this paper proposes a model that integrates textual information with structural response information from comments for the opinion tendency recognition task.