Research on multi-label classification method for terrorist news texts

Linying Li; Sunhe Wang; Yunping Qu

doi:10.1117/12.3035252

19 July 2024 Research on multi-label classification method for terrorist news texts

Linying Li, Sunhe Wang, Yunping Qu

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131F (2024) https://doi.org/10.1117/12.3035252
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Utilizing the pre-training model and the Text-CNN method, a classification system is constructed in order to propose a multi-label classification solution for terrorist news texts. The ultimate goal is to divide coarse-grained large categories into more detailed subcategories. The data set is selected from the Global Terrorism Database (GTD), and three labels, namely attack target, weapon type, and attack type, are selected in order to classify the text. Introduce a profound deep learning architecture by implementing ERNIE for extracting essential textual characteristics. Subsequently, the architecture is trained with a Convolutional Neural Network, which consists of diverse convolution kernel dimensions. The model suggested in this study surpasses other deep learning models concerning the GTD dataset. It has the potential to refine the granularity of text classification results, simplify subsequent research, and demonstrate substantial practical significance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linying Li, Sunhe Wang, and Yunping Qu "Research on multi-label classification method for terrorist news texts", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131F (19 July 2024); https://doi.org/10.1117/12.3035252

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