Embedding-Based Approaches to Hyperpartisan News Detection
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912470327623680 |
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| author | Mohan, Karthik |
| author_facet | Mohan, Karthik |
| contents | In this report, I describe the systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news takes an extremely polarized political standpoint with an intention of creating political divide among the public. Several approaches, including n-grams, sentiment analysis, as well as sentence and document representations using pre-tained ELMo models were used. The best system is using LLMs for embedding generation achieving an accuracy of around 92% over the previously best system using pre-trained ELMo with Bidirectional LSTM which achieved an accuracy of around 83% through 10-fold cross-validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embedding-Based Approaches to Hyperpartisan News Detection Mohan, Karthik Machine Learning Computation and Language In this report, I describe the systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news takes an extremely polarized political standpoint with an intention of creating political divide among the public. Several approaches, including n-grams, sentiment analysis, as well as sentence and document representations using pre-tained ELMo models were used. The best system is using LLMs for embedding generation achieving an accuracy of around 92% over the previously best system using pre-trained ELMo with Bidirectional LSTM which achieved an accuracy of around 83% through 10-fold cross-validation. |
| title | Embedding-Based Approaches to Hyperpartisan News Detection |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2501.01370 |