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

Abstract: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.

Accepted at PoliticalNLP 2026, the 3rd Workshop on Natural Language Processing for Political Sciences, co-located with LREC 2026. 10 pages, 3 figures

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.05155 [cs.CL]

(or arXiv:2608.05155v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.05155 Focus to learn more arXiv-issued DOI via DataCite

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