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        <title>Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation</title>
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        <namePart type="given">Maryam</namePart>
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            <title>Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)</title>
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            <namePart type="given">Haithem</namePart>
            <namePart type="family">Afli</namePart>
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            <namePart type="given">Wajdi</namePart>
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    <abstract>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 \ensuremath&lt; 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.</abstract>
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    <identifier type="doi">10.63317/2wbwmwq3jwfg</identifier>
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        <url>https://aclanthology.org/2026.politicalnlp-1.17/</url>
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%0 Conference Proceedings
%T Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation
%A Fooladi, Maryam
%A Bottino, Federico
%Y Afli, Haithem
%Y Bouamor, Houda
%Y Zaghouani, Wajdi
%Y Ghannay, Sahar
%Y Hossain, Shehenaz
%S Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F fooladi-bottino-2026-beyond
%X 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 \ensuremath< 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.
%R 10.63317/2wbwmwq3jwfg
%U https://aclanthology.org/2026.politicalnlp-1.17/
%U https://doi.org/10.63317/2wbwmwq3jwfg
%P 159-164

Markdown (Informal)

[Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation](https://aclanthology.org/2026.politicalnlp-1.17/) (Fooladi & Bottino, PoliticalNLP 2026)

• Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation (Fooladi & Bottino, PoliticalNLP 2026)

ACL

• Maryam Fooladi and Federico Bottino. 2026. Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation. In Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026), pages 159–164, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).