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

Sentiment analysis remains the dominant computational approach for evaluating political news, yet its ability to capture the rhetorical complexity of political discourse is increasingly questioned. This paper presents a systematic comparison between a transformer-based sentiment classifier (RoBERTa) and a Large Language Model-based multi-dimensional framing analysis framework across 50 political news articles from 17 international outlets. While RoBERTa classifies 70% of articles as neutral and reduces political discourse to a three-way polarity scale, the LLM-based framework captures 13 numerical dimensions including bias direction and intensity, manipulation indicators (cherry-picking, loaded language, false equivalence), sensationalism, and communicative intent. Our correlation analysis reveals only weak-to-moderate relationships between sentiment polarity and framing dimensions (maximum Pearson r = 0.38, p < 0.01), demonstrating that these approaches measure fundamentally different properties of political text. Through case studies, we show that sentiment-neutral articles can exhibit extreme manipulation patterns, while highly negative articles may reflect factual reporting on inherently negative events. These findings argue for moving beyond sentiment as a proxy for media quality, toward multi-dimensional frameworks that can reveal the rhetorical strategies invisible to polarity-based analysis. All data and analysis code will be made available upon acceptance.