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

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, d...

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Bibliographic Details
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
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13127221