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arXiv Open Access 2024

Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization

K. Lian L. S. Liebovitch M. Wild H. West P. T. Coleman +3 lainnya
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Abstrak

This paper presents a machine learning approach to classify countries as peaceful or non-peaceful using linguistic patterns extracted from global media articles. We employ vector embeddings and cosine similarity to develop a supervised classification model that effectively identifies peaceful countries. Additionally, we explore the impact of dataset size on model performance, investigating how shrinking the dataset influences classification accuracy. Our results highlight the challenges and opportunities associated with using large-scale text data for peace studies.

Topik & Kata Kunci

cs.CL cs.LG

Penulis (8)

K

K. Lian

L

L. S. Liebovitch

M

M. Wild

H

H. West

P

P. T. Coleman

F

F. Chen

E

E. Kimani

K

K. Sieck

Format Sitasi

APA MLA BibTeX
Lian, K., Liebovitch, L.S., Wild, M., West, H., Coleman, P.T., Chen, F. et al. (2024). Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization. https://arxiv.org/abs/2410.03749

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Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
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arXiv
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Open Access ✓