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Open Access

News Topic Detection Based on Capsule Semantic Graph

College of Computer and Information Science, Southwest University, Chongqing 400000, China
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Abstract

Most news topic detection methods use word-based methods, which easily ignore the relationship among words and have semantic sparsity, resulting in low topic detection accuracy. In addition, the current mainstream probability methods and graph analysis methods for topic detection have high time complexity. For these reasons, we present a news topic detection model on the basis of capsule semantic graph (CSG). The keywords that appear in each text at the same time are modeled as a keyword graph, which is divided into multiple subgraphs through community detection. Each subgraph contains a group of closely related keywords. The graph is used as the vertex of CSG. The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex. At the same time, the news text is clustered using the incremental clustering method, where each text uses CSG; that is, the similarity among texts is calculated by the graph kernel. The relationship between vertices and edges is also considered when calculating the similarity. Experimental results on three standard datasets show that CSG can obtain higher precision, recall, and F1 values than several latest methods. Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.

Keywords

news topic detectioncapsule semantic graphgraph kernel

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Big Data Mining and Analytics
Volume 5 Issue 2,
June 2022
Pages 98-109
DOI: 10.26599/BDMA.2021.9020023
Cite this article:
Yang S, Tang Y. News Topic Detection Based on Capsule Semantic Graph. Big Data Mining and Analytics, 2022, 5(2): 98-109. https://doi.org/10.26599/BDMA.2021.9020023

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Received: 05 November 2021
Accepted: 18 November 2021
Published: 25 January 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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