Title: Network public opinion hotspot topic mining method based on improved support vector machine

Authors: Yifan Wang

Addresses: Department of Health Management, Nanyang Medical College, Nanyang, 473000, China

Abstract: The research on mining hotspot topic in online public opinion is of great significance for improving social management efficiency and promoting economic development. In order to overcome the problems of low accuracy, low recall, and long response time in traditional methods, a network public opinion hotspot topic mining method based on improved support vector machine (SVM) is proposed. Utilise distributed web crawlers to collect network public opinion data, and extract features of the collected network public opinion data through adaptive domain relationships. Introduce the least squares method to improve the SVM, input the feature extraction results into the improved SVM, and obtain the mining results of network public opinion hotspot topic. The experimental results show that the accuracy of network public opinion hotspot topic mining using this method varies between 96.3% and 98.3%, with an average recall rate of 97.7% and a response time of 5.9 s.

Keywords: improve support vector machine; online public opinion; hotspot topic mining; distributed web crawlers; adaptive domain relationships; least squares method.

DOI: 10.1504/IJNVO.2025.145396

International Journal of Networking and Virtual Organisations, 2025 Vol.32 No.1/2/3/4, pp.273 - 290

Received: 18 Jun 2024
Accepted: 08 Nov 2024
Published online: 31 Mar 2025 *

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