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Model Framework for Discovering and Utilizing Public Opinion Hot Topic Knowledge in the Social Media Network Environment

Yun Liu

International Journal of Intelligent Information Technologies2025https://doi.org/10.4018/ijiit.372074article
ABDC C
Weight
0.37

What the paper says

The quick dissemination and nuanced nature of public opinion present additional difficulties for public opinion analysis in the age of social media's information explosion. Traditional public opinion analysis methods suffer from insufficient processing capabilities and single analysis methods, making it difficult to cope with large-scale and rapidly growing social media information. This article aims to utilize social media data sources and advanced algorithm models such as TextCNN (Text Convolutional Neural Network) and LSTM (Long Short-Term Memory) to construct a comprehensive model framework that addresses the limitations of traditional public opinion research and improves the accuracy, timeliness, and systematicity of public opinion hot topic knowledge discovery and utilization, thereby providing scientific basis for decision-making and optimizing the decision-making process.

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https://doi.org/https://doi.org/10.4018/ijiit.372074

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@article{yun2025,
  title        = {{Model Framework for Discovering and Utilizing Public Opinion Hot Topic Knowledge in the Social Media Network Environment}},
  author       = {Yun Liu},
  journal      = {International Journal of Intelligent Information Technologies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijiit.372074},
}

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Model Framework for Discovering and Utilizing Public Opinion Hot Topic Knowledge in the Social Media Network Environment

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.