Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917981638885376 |
|---|---|
| author | Lian, K. Liebovitch, L. S. Wild, M. West, H. Coleman, P. T. Chen, F. Kimani, E. Sieck, K. |
| author_facet | Lian, K. Liebovitch, L. S. Wild, M. West, H. Coleman, P. T. Chen, F. Kimani, E. Sieck, K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization Lian, K. Liebovitch, L. S. Wild, M. West, H. Coleman, P. T. Chen, F. Kimani, E. Sieck, K. Computation and Language Machine Learning 62H30 I.2.6 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. |
| title | Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization |
| topic | Computation and Language Machine Learning 62H30 I.2.6 |
| url | https://arxiv.org/abs/2410.03749 |