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

超越情感分析：传统自然语言处理与基于大语言模型的多维分析在政治新闻评估中的对比研究

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). 摘要： 传统的情感分析（SA）模型虽然在极性分类方面行之有效，但在洞察政治话语的修辞、意识形态和框架维度方面却能力有限——而这些维度正是社会科学与人文科学（SSH）研究的核心。

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. 本文展示了一项对比研究，我们将基于 RoBERTa 的情感分析与基于大语言模型（LLM）的多维框架分析平台进行了比较，并将其应用于来自 17 家国际媒体的 50 篇政治新闻语料库中。

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. 研究结果揭示了一个我们称之为“中立坍塌”（neutral collapse）的关键局限性：RoBERTa 将 70% 的文章归类为中立，实际上是将内容丰富的政治文本扁平化为一种缺乏分析价值的类别。我们发现，在被归类为中立的文章中，有 23% 的文章其负面概率得分超过了 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. 相比之下，基于大语言模型的方法能够捕捉到政治偏见的方向与强度、煽动性、情感诉求以及政治框架，从而产生符合社会科学与人文科学认识论的多维分析输出。

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. 我们认为，对于政治媒体分析而言，仅靠传统情感分析是不够的；基于大语言模型的多维框架为满足社会科学与人文科学的研究需求，提供了一种在认识论上更为适切的计算视角。