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Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation — arXiv:2608.05155

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05155
License (as archived): CC0-1.0
Archived: 2026-10-25T21:26:59.134Z
SHA-256 of the archived text: 5b7773fe52a172fe5246748e245554504021a9764ef7c0c13aa3b2b25382780f
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How to cite

Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation — arXiv:2608.05155 — arXiv (en), https://arxiv.org/abs/2608.05155 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f1019732fa4e (Metraipolis archive dc_f1019732fa4e, sha256 5b7773fe52a172fe5246748e245554504021a9764ef7c0c13aa3b2b25382780f)

BibTeX

@misc{dcf1019732fa4e,
  title = {Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation — arXiv:2608.05155},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05155},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f1019732fa4e; sha256 5b7773fe52a172fe5246748e245554504021a9764ef7c0c13aa3b2b25382780f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05155. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH). In this paper, we present a comparative study of RoBERTa-based sentiment analysis and an LLM-based multi-dimensional framing analysis platform applied to a corpus of 50 political news articles from 17 international media outlets. The results reveal a critical limitation we term neutral collapse: RoBERTa classifies 70% of articles as neutral, effectively flattening substantively rich political content into an analytically uninformative category. We find that 23% of neutral-classified articles exhibit negative probability scores above 0.30. By contrast, the LLM-based approach captures political bias direction and intensity, sensationalism, emotional appeal, and political framing -- yielding multi-dimensional analytical outputs aligned with SSH epistemologies. We argue that for political media analysis, traditional SA alone is insufficient, and that LLM-based multi-dimensional frameworks offer a more epistemologically adequate computational lens for SSH research needs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-07 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.05155 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T21:24:38.164Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).