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基于大数据的多属性网络舆情预测方法.

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Title: 基于大数据的多属性网络舆情预测方法.
Alternate Title: A multi-attribute network public opinion prediction method based on big data.
Authors: 帕丽旦1 paridam@aliyun.com, 木合塔尔1 gwq@xjfeu.com, 郭文强1 498841300@qq.com, 路 翀1
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Apr2026, Vol. 48 Issue 4, p752-760. 9p.
Subjects: Multiple criteria decision making, Association rule mining, Sentiment analysis, Big data, Data scrubbing, Risk assessment, Language models
Abstract (English): To quantitatively analyze the ability of social media network public opinion control, a network public opinion risk prediction method based on multi-attribute decision-making and comprehensive weight analysis is proposed. Firstly, web crawling methods are employed for data collection, and antiinterference matched filtering methods are used to clean the collected network public opinion data. Secondly, based on the preprocessed network media public opinion data, a multi-attribute comprehensive decision object model is constructed to obtain multiple quantifiable attribute sets, and word segmentation technology is used to decompose the text data into words. Based on the segmentation results, the association rules between the evolution of public opinion risks and people's preferences are explored, and then the degree of association is calculated. Finally, the degree of association is fed as input into the BERT pre-trained vector model to obtain the directed feature values of network public opinion risks. By leveraging the evolutionary characteristics of network public opinion risks, predictions of their evolution are achieved. Simulation results demonstrate that the proposed method exhibits strong optimization capabilities in predicting the evolution of network public opinion risks. The F1 comprehensive evaluation metric has improved compared to the standard methods, enhancing the accuracy of public opinion classification. Moreover, the prediction accuracy for the evolution of public opinion risks reached 97.6%.. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 为量化分析社交媒体网络舆情控制能力,提出基于多属性决策和综合权重分析的网络舆情风 险预测方法。首先,选择网络爬虫方法进行数据采集,对采集到的网络舆情数据采用抗干扰的匹配滤波方 法对其进行数据清洗。其次,针对预处理后的网络媒体舆情数据,构建多属性综合决策对象模型,以获取 多个可量化的属性集合,并采用分词技术将文本数据分解为词语。基于分词结果,挖掘出舆情风险演化与 人们喜好之间的关联规则,进而计算得到关联度。最后,将关联度作为BERT 预训练向量模型的输入,获 取网络舆情风险指向特征值,利用网络舆情风险演化特征实现网络舆情风险演化预测。仿真结果表明,所 提方法进行网络舆情风险演化预测的寻优能力较强,F1 综合评价指标比标准方法有所提高,提高了舆情 分类的准确性,并且舆情风险演化预测精度达到了97.6%。. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 基于大数据的多属性网络舆情预测方法.
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  Data: A multi-attribute network public opinion prediction method based on big data.
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  Data: <searchLink fieldCode="AR" term="%22帕丽旦%22">帕丽旦</searchLink><relatesTo>1</relatesTo><i> paridam@aliyun.com</i><br /><searchLink fieldCode="AR" term="%22木合塔尔%22">木合塔尔</searchLink><relatesTo>1</relatesTo><i> gwq@xjfeu.com</i><br /><searchLink fieldCode="AR" term="%22郭文强%22">郭文强</searchLink><relatesTo>1</relatesTo><i> 498841300@qq.com</i><br /><searchLink fieldCode="AR" term="%22路+翀%22">路 翀</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+scrubbing%22">Data scrubbing</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: To quantitatively analyze the ability of social media network public opinion control, a network public opinion risk prediction method based on multi-attribute decision-making and comprehensive weight analysis is proposed. Firstly, web crawling methods are employed for data collection, and antiinterference matched filtering methods are used to clean the collected network public opinion data. Secondly, based on the preprocessed network media public opinion data, a multi-attribute comprehensive decision object model is constructed to obtain multiple quantifiable attribute sets, and word segmentation technology is used to decompose the text data into words. Based on the segmentation results, the association rules between the evolution of public opinion risks and people's preferences are explored, and then the degree of association is calculated. Finally, the degree of association is fed as input into the BERT pre-trained vector model to obtain the directed feature values of network public opinion risks. By leveraging the evolutionary characteristics of network public opinion risks, predictions of their evolution are achieved. Simulation results demonstrate that the proposed method exhibits strong optimization capabilities in predicting the evolution of network public opinion risks. The F1 comprehensive evaluation metric has improved compared to the standard methods, enhancing the accuracy of public opinion classification. Moreover, the prediction accuracy for the evolution of public opinion risks reached 97.6%.. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 为量化分析社交媒体网络舆情控制能力,提出基于多属性决策和综合权重分析的网络舆情风 险预测方法。首先,选择网络爬虫方法进行数据采集,对采集到的网络舆情数据采用抗干扰的匹配滤波方 法对其进行数据清洗。其次,针对预处理后的网络媒体舆情数据,构建多属性综合决策对象模型,以获取 多个可量化的属性集合,并采用分词技术将文本数据分解为词语。基于分词结果,挖掘出舆情风险演化与 人们喜好之间的关联规则,进而计算得到关联度。最后,将关联度作为BERT 预训练向量模型的输入,获 取网络舆情风险指向特征值,利用网络舆情风险演化特征实现网络舆情风险演化预测。仿真结果表明,所 提方法进行网络舆情风险演化预测的寻优能力较强,F1 综合评价指标比标准方法有所提高,提高了舆情 分类的准确性,并且舆情风险演化预测精度达到了97.6%。. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3969/j.issn.1007-130X.2026.04.019
    Languages:
      – Code: chi
        Text: Chinese
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 752
    Subjects:
      – SubjectFull: Multiple criteria decision making
        Type: general
      – SubjectFull: Association rule mining
        Type: general
      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Data scrubbing
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Language models
        Type: general
    Titles:
      – TitleFull: 基于大数据的多属性网络舆情预测方法.
        Type: main
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    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: 帕丽旦
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            NameFull: 木合塔尔
      – PersonEntity:
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            NameFull: 郭文强
      – PersonEntity:
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            NameFull: 路 翀
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          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 48
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            – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue
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