GunadarmaXBRIN at SemEval-2023 Task 12: Utilization of SVM and AfriBERTa for Monolingual, Multilingual, and Zero-shot Sentiment Analysis in African Languages
Novitasari Arlim, Slamet Riyanto, Rodiah Rodiah, Al Hafiz Akbar Maulana Siagian
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 12: afrisenti-semeval: sentiment analysis for low-resource african languages using twitter dataset Paper
TLDR:
This paper describes our participation in Task 12: AfriSenti-SemEval 2023, i.e., track 12 of subtask A, track 16 of subtask B, and track 18 of subtask C. To deal with these three tracks, we utilize Support Vector Machine (SVM) + One vs Rest, SVM + One vs Rest with SMOTE, and AfriBERTa-large models.
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Abstract:
This paper describes our participation in Task 12: AfriSenti-SemEval 2023, i.e., track 12 of subtask A, track 16 of subtask B, and track 18 of subtask C. To deal with these three tracks, we utilize Support Vector Machine (SVM) + One vs Rest, SVM + One vs Rest with SMOTE, and AfriBERTa-large models. In particular, our SVM + One vs Rest with SMOTE model could obtain the highest weighted F1-Score for tracks 16 and 18 in the evaluation phase, that is, 65.14\% and 33.49\%, respectively. Meanwhile, our SVM + One vs Rest model could perform better than other models for track 12 in the evaluation phase.