Beyond Sentiment — Federico Bottino

Why Traditional NLP Fails Political News — and How LLMs Can Bridge the Gap

Maryam Fooladi, Federico Bottino

Paper, 3rd Workshop on NLP for Political Sciences (PoliticalNLP 2026) at LREC 2026 — Palma de Mallorca, 2026

Abstract

Traditional sentiment analysis collapses politically substantive news into a 'neutral' bin in roughly 70% of cases, missing framing, ideology, and rhetorical strategy. We compare classical NLP pipelines with LLM-based approaches and show how LLMs can recover the analytic dimensions social scientists actually need.

RoBERTa label distribution on 50 political news articles: 70% neutral, 26% negative, 4% positive. Mean probabilities P(neg) 0.291, P(neu) 0.638, P(pos) 0.070. — Figure 1 — RoBERTa on a 50-article corpus from 17 outlets (paper §4.1). 70% of substantively political coverage collapses into 'neutral'; positive is effectively suppressed across the entire corpus.

Three RoBERTa-neutral political news articles with high P(neg): BBC on Australia's first female Liberal leader (P(neg) 0.48, margin 0.02), Mexico-UK diplomatic tensions (P(neg) 0.43), Franco-German military disagreements (P(neg) 0.41). — Figure 2 — Three articles RoBERTa called 'neutral' despite high P(neg) (paper §4.2). The Australia case sits 2 points from the decision boundary; 8 of 35 neutral articles in the corpus carry P(neg) above 0.30.