Explorando meta-variáveis para a classificação de persuasão em textos de memes políticos
DOI:
https://doi.org/10.5753/reic.2026.6962Keywords:
meta-varíaveis, persuasão, classificação de memes, classificação de textos curtos, aprendizado de máquinaAbstract
Este trabalho propõe uma abordagem baseada em engenharia de características para detectar estratégias persuasivas em textos de memes políticos. Foram definidos quatro grupos de meta-variáveis: (i) retóricas, (ii) de sentimento e discurso de ódio, (iii) estruturais e (iv) contextuais. Os experimentos utilizaram a base da Task 4 da competição SemEval 2024, com 7.000 instâncias de treino e 1.000 de teste. Os algoritmos Floresta Aleatória e Regressão Logística foram avaliados com e sem o tratamento do desbalanceamento entre as classes da base de dados de treinamento. O melhor resultado, com F1-macro de 0,701, foi obtido pela combinação dos grupos de meta-variáveis retóricas e estruturais. A abordagem proposta oferece uma alternativa computacionalmente eficiente e interpretável frente aos modelos baseados em redes neurais profundas.
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