超越情感：传统NLP与LLM多维分析在政治新闻评估中的比较
原标题：Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation
AI 摘要
本研究对比了基于RoBERTa的传统情感分析与基于LLM的多维框架分析在政治新闻评估中的表现。结果显示，RoBERTa将70%的文章归类为中性，导致信息扁平化，而LLM方法能捕捉政治偏见、煽情性、情感诉求和框架等维度。作者认为，对于政治媒体分析，传统情感分析不足，LLM多维框架更适合社会科学和人文学科研究。
正文节选
Computer Science > Computation and Language Title:Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation View PDF HTML (experimental) 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 t