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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Knowledge graph-enhanced residual ensemble method for online pub-lic opinion popularity prediction

Yang Min1
Li Mingwu1
Peng Guoli1
Wu Fanglong2
1. Library, Xihua University, Chengdu 610000, China
2. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610000, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0257

Abstract

To address the challenge of integrating multi-source heterogeneous data and capturing deep semantic correlations in public opinion prediction, this study proposes a Knowledge Graph-enhanced Auto-Residual Regression Ensemble (KG-ARRE) framework. The framework extracted daily features from microblog data, built a subject–object interaction knowledge graph using textual semantic analysis, and applied an Auto-Regressive Integrated Moving Average (ARIMA) model to obtain residuals from the popularity time series. A Temporal Convolutional Network (TCN) then combined the multi-source features for modeling. The framework also used multiple randomly initialized sub-models and an error-weighted ensemble mechanism to improve prediction stability. Experiments on real-event datasets showed that KG-ARRE achieved a mean absolute percentage error (MAPE) of 4.53%, which was 63.1% lower than that of the baseline TCN model (MAPE = 12.28%) . When the knowledge graph and ensemble modules were transferred to models such as BiLSTM, MAPE decreased by 2.36–7.05 percentage points. These results demonstrate that KG-ARRE enhances prediction accuracy and generalization.

Foundation Support

四川省哲学社会科学基金资助项目(SCJJ23ND12)
四川省社会科学重点研究基地资助项目(SCAA25-B12,SCAA24-B17)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0257
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-17] Accepted Paper

Cite This Article

杨敏, 李明伍, 彭国莉, 等. 知识图谱增强的残差集成网络舆情热度预测方法 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0257. (Yang Min, Li Mingwu, Peng Guoli, et al. Knowledge graph-enhanced residual ensemble method for online pub-lic opinion popularity prediction [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0257. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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