Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features

• Published in: Applied sciences 2023-06, Vol.13 (12), p.7221
• Main Authors: Long, Jie, Li, Zihan, Xuan, Qi, Fu, Chenbo, Peng, Songtao, Min, Yong
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Annotations; Classification; Computational linguistics; Datasets; Decision trees; Dictionaries; Feature recognition; graph embedding; graph neural network; Graph neural networks; Internet; Language processing; Methods; Natural language interfaces; natural language process; Natural language processing; Neural networks; opinion tendency recognition; Sentiment analysis; Social media; social network; Social networks; Text categorization
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13127221

The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recognition on the unannotated statements. However, due to the uncertainty of NLP (natural language processing) in short scenes and the complexity of Chinese text, existing methods have some limitations in accuracy and application scenarios. In this paper, we propose an opinion tendency recognition model HGAT (heterogeneous graph attention network) that integrates text vector and context structure methods to address the above problems. This method first trains a text vectorization model based on annotation text content, then constructs an isomorphic graph with annotation, news, and theme as its apex, and then optimizes the feature vectors of all nodes using an isomorphic graph neural network model with attention mechanism. In addition, this article collected 1,684,318 news items and 57,845,091 comments based on Toutiao, sifted through 511 of those stories and their corresponding 103,787 comments, and tested the impact of HGAT on this dataset. Experiments show that this method has stable improvement effect on different NLP methods, increasing accuracy by 2–10%, and provides a new perspective for opinion tendency recognition.