@inproceedings{gokani-etal-2023-witcherses,
    title = "Witcherses at {S}em{E}val-2023 Task 12: Ensemble Learning for {A}frican Sentiment Analysis",
    author = "Gokani, Monil  and
      Srivatsa, K V Aditya  and
      Mamidi, Radhika",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/2023.semeval-1.48/",
    doi = "10.18653/v1/2023.semeval-1.48",
    pages = "357--364",
    abstract = "This paper describes our system submission for SemEval-2023 Task 12 AfriSenti-SemEval: Sentiment Analysis for African Languages. We propose an XGBoost-based ensemble model trained on emoticon frequency-based features and the predictions of several statistical models such as SVMs, Logistic Regression, Random Forests, and BERT-based pre-trained language models such as AfriBERTa and AfroXLMR. We also report results from additional experiments not in the system. Our system achieves a mixed bag of results, achieving a best rank of 7th in three of the languages - Igbo, Twi, and Yoruba."
}