@inproceedings{keinan-hacohen-kerner-2023-jct,
    title = "{JCT} at {S}em{E}val-2023 Tasks 12 A and 12{B}: Sentiment Analysis for Tweets Written in Low-resource {A}frican Languages using Various Machine Learning and Deep Learning Methods, Resampling, and {H}yper{P}arameter Tuning",
    author = "Keinan, Ron  and
      Hacohen-Kerner, Yaakov",
    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-1.49/",
    doi = "10.18653/v1/2023.semeval-1.49",
    pages = "365--378",
    abstract = "In this paper, we describe our submissions to the SemEval-2023 contest. We tackled subtask 12 - {\textquotedblleft}AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset{\textquotedblright}. We developed different models for 12 African languages and a 13th model for a multilingual dataset built from these 12 languages. We applied a wide variety of word and char n-grams based on their tf-idf values, 4 classical machine learning methods, 2 deep learning methods, and 3 oversampling methods. We used 12 sentiment lexicons and applied extensive hyperparameter tuning."
}