The Status and Trend of Chinese News Forecast Based on Graph Convolutional Network Pooling Algorithm
Source abstract
Abstract
It is always a hot issue in the intelligence analysis field to predict the trend of news description by pre-trained language models and graph neural networks. However, there are several problems in the existing research: (1) there are few Chinese data sets on this subject in academia and industry; and (2) using the existing pre-trained language models and graph classification algorithms cannot achieve satisfactory results. The method described in this paper can better solve these problems. (1) We built a Chinese news database predicted by more than 9000 annotated news time trends, filling the gaps in this database. (2) We designed an improved method based on the pre-trained language model and graph neural networks pooling algorithm. In the graph pooling algorithm, the Graph U-Nets Pooling method and self-attention are combined, which can better solve the analysis of the problem of forecasting the development trend of news events. The experimental results show that the effect of this method compared with the baseline graph classification algorithm is improved, and it also solves the shortcomings of the pre-trained language model that cannot handle very long texts. Therefore, it can be concluded that our research has strong processing capabilities for analyzing and predicting the development trend of Chinese news events.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks · Complex Network Analysis Techniques · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
References (21)
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