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This paper presents a novel computational approach to analyzing Brexit-related discourse in British media through the application of fuzzy logic principles. The proposed framework addresses the inherent ambiguity and complexity in political discourse by quantifying three key linguistic dimensions: sentiment polarity, argumentative strength, and rhetorical intensity. Unlike traditional binary classification systems, our fuzzy inference system (FIS) acknowledges the gradient nature of media bias through a comprehensive set of membership functions and fuzzy rules. The model employs a Mamdani-type FIS implemented in MATLAB’s Fuzzy Logic Toolbox, enabling systematic evaluation of media texts along a continuous spectrum from balanced to highly biased reporting. Through experimental validation on a corpus of 200 Brexit-related articles from major British publications, we demonstrate that the proposed framework achieves 78% agreement with expert human analysts in bias assessment tasks, significantly outperforming conventional sentiment analysis approaches. This research contributes to the growing field of computational pragmatics by offering a mathematically rigorous yet linguistically nuanced method for discourse analysis that captures the multidimensional nature of political communication. The framework has potential applications in media literacy education, journalistic practice, and political communication research.

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A Fuzzy Logic-Based Framework for Pragmatic Analysis of Brexit-Related Discourse in Contemporary British Media Texts

  • 会议论文

  • 发表日期：

    2025年

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  • Rahib Imamguluyev,
  • Targul Alizade,
  • Tunzala Imanova,
  • Aytaj Osmanova,
  • Aysel Mamedova,
  • Chinara Narimanova,
  • Shahana Musayeva,
  • Susangul Islamova

摘要

This paper presents a novel computational approach to analyzing Brexit-related discourse in British media through the application of fuzzy logic principles. The proposed framework addresses the inherent ambiguity and complexity in political discourse by quantifying three key linguistic dimensions: sentiment polarity, argumentative strength, and rhetorical intensity. Unlike traditional binary classification systems, our fuzzy inference system (FIS) acknowledges the gradient nature of media bias through a comprehensive set of membership functions and fuzzy rules. The model employs a Mamdani-type FIS implemented in MATLAB’s Fuzzy Logic Toolbox, enabling systematic evaluation of media texts along a continuous spectrum from balanced to highly biased reporting. Through experimental validation on a corpus of 200 Brexit-related articles from major British publications, we demonstrate that the proposed framework achieves 78% agreement with expert human analysts in bias assessment tasks, significantly outperforming conventional sentiment analysis approaches. This research contributes to the growing field of computational pragmatics by offering a mathematically rigorous yet linguistically nuanced method for discourse analysis that captures the multidimensional nature of political communication. The framework has potential applications in media literacy education, journalistic practice, and political communication research.