发表机构
Kakashi Ventures Accelerator (KVA); Newjee（卡卡西风险投资加速器（KVA）; 新吉）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文对比传统NLP的RoBERTa情感分析与基于LLM的多维框架分析，发现前者存在“中性坍缩”局限，后者可捕捉政治话语多维特征，更适配SSH研究需求。
AI 中文摘要
传统情感分析（SA）模型虽在极性分类方面有效，但对政治话语的修辞、意识形态及框架维度的洞察有限，而这些维度是社会科学与人文（SSH）研究的核心。本文针对来自17家国际媒体的50篇政治新闻文章组成的语料库，开展了基于RoBERTa的情感分析与基于大语言模型（LLM）的多维框架分析平台的对比研究。结果显示存在一种被称为“中性坍缩”的关键局限：RoBERTa将70%的文章归类为中性，实则将内容丰富的政治内容简化为分析上无信息的类别；我们还发现，23%被归类为中性的文章，其负向概率得分高于0.30。相比之下，基于LLM的方法可捕捉政治偏见的方向与强度、煽情主义、情感诉求及政治框架，生成符合SSH认识论的多维分析输出。本文认为，针对政治媒体分析，仅靠传统SA是不够的，基于LLM的多维框架为SSH研究需求提供了在认识论上更具适配性的计算视角。
英文摘要
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.
CommentsAccepted at PoliticalNLP 2026, the 3rd Workshop on Natural Language Processing for Political Sciences, co-located with LREC 2026. 10 pages, 3 figures
Journal ref2026.politicalnlp-1.17
