2024
DOI: 10.21203/rs.3.rs-3978507/v1
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Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification

Samawel Jaballi,
Salah Zrigui,
Henri Nicolas
et al.

Abstract: This study confronts the challenge of analyzing multilingual, code-switched conversations during the COVID-19 pandemic, a context where traditional classifiers often fall short. We developed a cost-sensitive ensemble learning approach that combines a reweighted AdaBoost-SVM model with an SVM as its base learner, specifically designed to effectively manage the imbalanced dataset common in code-switched communication scenarios. A key innovation of our approach is the novel rebalancing of AdaBoost weights. By inc… Show more

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