Early warning of college students' ideological public opinion based on TF-IDF and RFB neural network
Year: 2026
Type: article
Abstract: To address the need for real-time early warning of college students' social media opinions, this study proposes a dynamic model integrating term frequency-inverse document frequency (TF-IDF) feature weighting and radial basis function (RBF) neural networks.A subset of 32,715 college-student comments from Tsinghua University's Weibo-100k dataset serves as training samples, with cross-domain validation performed using the ChnSentiCorp benchmark.The approach optimises text feature sparsity via TF-IDF and utilises the nonlinear classification capability of RBF networks for opinion risk categorisation.Experimental results demonstrate an F1-score of 89.7% on the test set -marking a 6.2% improvement over conventional long short-term memory networks -while reducing warning response latency to 12 ms.This confirms high accuracy and real-time performance, providing a lightweight solution for monitoring campus ideological dynamics.
Author: Guixue Tan
Institution: Shandong Huayu University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7131358085
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