@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://aclanthology.org/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 - {``}AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset{''}. 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.",
}
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<abstract>In this paper, we describe our submissions to the SemEval-2023 contest. We tackled subtask 12 - “AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset”. 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.</abstract>
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<url>https://aclanthology.org/2023.semeval-1.49</url>
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%0 Conference Proceedings
%T JCT at SemEval-2023 Tasks 12 A and 12B: Sentiment Analysis for Tweets Written in Low-resource African Languages using Various Machine Learning and Deep Learning Methods, Resampling, and HyperParameter Tuning
%A Keinan, Ron
%A Hacohen-Kerner, Yaakov
%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 keinan-hacohen-kerner-2023-jct
%X In this paper, we describe our submissions to the SemEval-2023 contest. We tackled subtask 12 - “AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset”. 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.
%R 10.18653/v1/2023.semeval-1.49
%U https://aclanthology.org/2023.semeval-1.49
%U https://doi.org/10.18653/v1/2023.semeval-1.49
%P 365-378
Markdown (Informal)
[JCT at SemEval-2023 Tasks 12 A and 12B: Sentiment Analysis for Tweets Written in Low-resource African Languages using Various Machine Learning and Deep Learning Methods, Resampling, and HyperParameter Tuning](https://aclanthology.org/2023.semeval-1.49) (Keinan & Hacohen-Kerner, SemEval 2023)
ACL