传统情感分析会掩盖政治新闻的深层含义，新方法能多维度解析舆论立场。
Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation
- 用大模型替代传统情感分析，实现政治立场、修辞风格等多维度解析
- 70%的政治新闻被传统模型误判为中性，其中23%实际有强烈负面倾向
- 适合社科人文研究者，可揭示媒体偏见与话语策略
传统情感分析（SA）虽能判断情绪极性，却难以捕捉政治话语中的修辞、意识形态和框架特征——这些正是社会科学与人文学科（SSH）研究的核心。本文对比了基于RoBERTa的情感分析与基于大模型的多维框架分析平台，在来自17家国际媒体的50篇政治新闻上进行测试。结果显示存在‘中性坍塌’现象：RoBERTa将70%的文章分类为中性，导致丰富政治内容被简化为无分析价值的类别；其中23%的中性文章仍具有超过0.30的负面概率得分。而大模型方法能有效识别政治偏见方向与强度、煽动性、情感诉求及政治框架，输出结果更符合SSH研究的认知范式。我们主张，仅靠传统情感分析无法满足政治媒体分析需求，大模型驱动的多维分析框架才是更适配的计算工具。
原文摘要 · Abstract (English)
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.
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