Exploring Word Embedding for Arabic Sentiment Analysis

Gayed, Sana; Mallat, Souheyl; Zrigui, Mounir

doi:10.1007/978-981-19-8234-7_8

Cited by 2 publications

(1 citation statement)

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“…However, the efficacy of these traditional methods is often compromised in the face of imbalanced datasets, a scenario increasingly encountered in the analysis of pandemic-era communications. These imbalanced datasets, where standard models might overlook less represented cases, necessitate a reconsideration of traditional classification approaches [2]. As data mining and analysis evolve, the issue of class imbalance in datasets has gained prominence, prompting extensive academic research.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification

Jaballi,

Zrigui,

Nicolas

et al. 2024

Preprint

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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 incrementally adjusting the weights of misclassified samples from both minority and majority classes, we achieve a more balanced classification in each iteration. This strategy significantly improves the accuracy for minority class classification, a common issue with existing models. In the testing phase, we employed a comprehensive selection of both machine and deep learning classifiers, including Naive Bayes, Decision Trees, SMOTEBoost, CNN, Bi-LSTM, etc. These classifiers underwent comprehensive evaluation across two different multilingual datasets, assessed using six distinct metrics, including P-mean. The results from our experiments demonstrate that our proposed ensemble learning approach, fine-tuned with optimal hyperparameters and leveraging M-BERT for feature extraction, achieved remarkable accuracies of 78.84%, 86.56% and 83.96% on the test sets of the CTSA, TUNIZI and combined CTSA-TUNIZI datasets, respectively. This performance not only surpassed traditional classification methods but also outperformed advanced deep learning models, such as Bi-LSTM.

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Section: Introductionmentioning

confidence: 99%