@inproceedings{garcia-diaz-etal-2023-umuteam,
    title = "{UMUT}eam at {S}em{E}val-2023 Task 12: Ensemble Learning of {LLM}s applied to Sentiment Analysis for Low-resource {A}frican Languages",
    author = "Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio  and
      Caparros-laiz, Camilo  and
      Almela, {\'A}ngela  and
      Alcar{\'a}z-M{\'a}rmol, Gema  and
      Mar{\'\i}n-P{\'e}rez, Mar{\'\i}a Jos{\'e}  and
      Valencia-Garc{\'\i}a, Rafael",
    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-1.38",
    doi = "10.18653/v1/2023.semeval-1.38",
    pages = "285--292",
    abstract = "These working notes summarize the participation of the UMUTeam in the SemEval 2023 shared task: AfriSenti, focused on Sentiment Analysis in several African languages. Two subtasks are proposed, one in which each language is considered separately and another one in which all languages are merged. Our proposal to solve both subtasks is grounded on the combination of features extracted from several multilingual Large Language Models and a subset of language-independent linguistic features. Our best results are achieved with the African languages less represented in the training set: Xitsonga, a Mozambique dialect, with a weighted f1-score of 54.89{\textbackslash}{\%}; Algerian Arabic, with a weighted f1-score of 68.52{\textbackslash}{\%}; Swahili, with a weighted f1-score of 60.52{\textbackslash}{\%}; and Twi, with a weighted f1-score of 71.14{\%}.",
}

These working notes summarize the participation of the UMUTeam in the SemEval 2023 shared task: AfriSenti, focused on Sentiment Analysis in several African languages. Two subtasks are proposed, one in which each language is considered separately and another one in which all languages are merged. Our proposal to solve both subtasks is grounded on the combination of features extracted from several multilingual Large Language Models and a subset of language-independent linguistic features. Our best results are achieved with the African languages less represented in the training set: Xitsonga, a Mozambique dialect, with a weighted f1-score of 54.89{\textbackslash}{\%}; Algerian Arabic, with a weighted f1-score of 68.52{\textbackslash}{\%}; Swahili, with a weighted f1-score of 60.52{\textbackslash}{\%}; and Twi, with a weighted f1-score of 71.14{\%}.
Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio Caparros-laiz, Camilo Almela, {\'A}ngela Alcar{\'a}z-M{\'a}rmol, Gema Mar{\'\i}n-P{\'e}rez, Mar{\'\i}a Jos{\'e} Valencia-Garc{\'\i}a, Rafael
UMUTeam at SemEval-2023 Task 12: Ensemble Learning of LLMs applied to Sentiment Analysis for Low-resource African Languages