@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Witcherses at SemEval-2023 Task 12: Ensemble Learning for African Sentiment Analysis
%A Gokani, Monil
%A Srivatsa, K. V. Aditya
%A Mamidi, Radhika
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gokani-etal-2023-witcherses
%X 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.
%R 10.18653/v1/2023.semeval-1.48
%U https://aclanthology.org/2023.semeval-1.48
%U https://doi.org/10.18653/v1/2023.semeval-1.48
%P 357-364
Markdown (Informal)
[Witcherses at SemEval-2023 Task 12: Ensemble Learning for African Sentiment Analysis](https://aclanthology.org/2023.semeval-1.48) (Gokani et al., SemEval 2023)
ACL