# Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features
## 2 Related Work
### Opinion Tendency Recognition  
Opinion tendency recognition is a subtask of sentiment classification, which is primarily concerned with the problem of text classification. The purpose of this task is to judge whether a statement about a target expresses a positive, negative, or neutral attitude toward the target. Early traditional methods relied primarily on dictionary models to perform feature counting on textual phrases, such as computing feature values via text decomposition, keyword extraction, and so on, and then determining the opinion-orientation of the text based on the feature values. Early research not only used dictionaries to count feature information in text, but also applied machine learning methods like Support Vector Machines (SVM) [8], decision trees [9; 10] and other methods, which are primarily statistical methods for the analysis of opinion guidance. Most of these methods first compute sentence features based on text features and then combine all of the features into a vector. Lastly, they train classifiers such as SVMs and decision trees to achieve opinion-driven text classification. The aforementioned methods, however, require manual design of feature computation methods and statistical analysis, which is time-consuming and labor-intensive. In contrast, neural network methods compensate for the shortcomings of the above methods. The success of neural networks in image and speech recognition has led to the gradual application of some related models to position detection. For example, in recent years, a variety of methods have been proposed that incorporate textual information into CNN and RNN models for sentiment analysis [11; 12; 13; 14]. Apart from CNN and RNN models, LSTM has also been shown to have significant effects in opinion tendency recognition. [PERSON] et al. [15] proposed a nested BiLSTM and LSTM model structure in order to learn information from a larger set of contextual texts. Furthermore, they used an attention mechanism to magnify the impact of salient information content, which further improved the accuracy of the results. [PERSON] et al. [16] augmented the model structure of memristive neural networks using LSTM and CNNs, and introduced a similarity matrix for comparing content similarity across context, improving the prediction accuracy of the model. With the introduction of the BERT model in recent years [17], a large number of Transformer based models have demonstrated significant performance on NLP tasks. Some recent work has also investigated the effectiveness of the BERT model for opinion tendency recognition. For example, [PERSON] et al. [18] compared the performance of the BERT model with other methods on the SemEval2016 dataset and demonstrated that the BERT model has the best performance. [PERSON] et al. [19] also studied the effectiveness of the BERT model for data augmentation, and found that the BERT model performs exceptionally well in opinion tendency recognition. For sentiment classification in the Chinese domain, scholars have optimized different aspects based on the mainstream methods. Chinese text opinion mining research started relatively late, and the sentiment lexicon and deactivation lexicon are not as rich as English. To solve this problem, [PERSON] et al. [20] constructed an extended sentiment dictionary. The extended sentiment dictionary contains basic sentiment words, domain sentiment words and multisense sentiment words, which improves the accuracy of sentiment analysis. A plain Bayesian classifier is used to determine the text domain in which the multisense sentiment words are located. To address the problem that neural network models cannot accurately capture sentiment information in sentiment analysis tasks, [PERSON] et al. [21] proposed a sentiment information-based network model (SINM) that uses converter encoders and LSTM as model components to automatically find sentiment knowledge in Chinese texts with the help of Chinese sentiment dictionaries. [PERSON] et al.