@inproceedings{rathi-etal-2023-trinity,
    title = "Trinity at {S}em{E}val-2023 Task 12: Sentiment Analysis for Low-resource {A}frican Languages using {T}witter Dataset",
    author = "Rathi, Shashank  and
      Pande, Siddhesh  and
      Atkare, Harshwardhan  and
      Tangsali, Rahul  and
      Vyawahare, Aditya  and
      Kadam, Dipali",
    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-1.161",
    doi = "10.18653/v1/2023.semeval-1.161",
    pages = "1161--1165",
    abstract = "In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.",
}

In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.
Rathi, Shashank P e, Siddhesh Atkare, Harshwardhan Tangsali, Rahul Vyawahare, Aditya Kadam, Dipali
Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset