@inproceedings{arlim-etal-2023-gunadarmaxbrin,
    title = "{G}unadarma{XBRIN} at {S}em{E}val-2023 Task 12: Utilization of {SVM} and {A}fri{BERT}a for Monolingual, Multilingual, and Zero-shot Sentiment Analysis in {A}frican Languages",
    author = "Arlim, Novitasari  and
      Riyanto, Slamet  and
      Rodiah, Rodiah  and
      Siagian, Al Hafiz Akbar Maulana",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.120",
    doi = "10.18653/v1/2023.semeval-1.120",
    pages = "869--877",
    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.",
}

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.
Arlim, Novitasari Riyanto, Slamet Rodiah, Rodiah Siagian, Al Hafiz Akbar Maulana
GunadarmaXBRIN at SemEval-2023 Task 12: Utilization of SVM and AfriBERTa for Monolingual, Multilingual, and Zero-shot Sentiment Analysis in African Languages