@inproceedings{qaddoumi-2022-arabic,
title = "{A}rabic Sentiment Analysis by Pretrained Ensemble",
author = "Qaddoumi, Abdelrahim",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.47",
doi = "10.18653/v1/2022.wanlp-1.47",
pages = "447--451",
abstract = "This paper presents the 259 team{'}s BERT ensemble designed for the NADI 2022 Subtask 2 (sentiment analysis) (Abdul-Mageed et al., 2022). Twitter Sentiment analysis is one of the language processing (NLP) tasks that provides a method to understand the perception and emotions of the public around specific topics. The most common research approach focuses on obtaining the tweet{'}s sentiment by analyzing its lexical and syntactic features. We used multiple pretrained Arabic-Bert models with a simple average ensembling and then chose the best-performing ensemble on the training dataset and ran it on the development dataset. This system ranked 3rd in Subtask 2 with a Macro-PN-F1-score of 72.49{\%}.",
}
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<abstract>This paper presents the 259 team’s BERT ensemble designed for the NADI 2022 Subtask 2 (sentiment analysis) (Abdul-Mageed et al., 2022). Twitter Sentiment analysis is one of the language processing (NLP) tasks that provides a method to understand the perception and emotions of the public around specific topics. The most common research approach focuses on obtaining the tweet’s sentiment by analyzing its lexical and syntactic features. We used multiple pretrained Arabic-Bert models with a simple average ensembling and then chose the best-performing ensemble on the training dataset and ran it on the development dataset. This system ranked 3rd in Subtask 2 with a Macro-PN-F1-score of 72.49%.</abstract>
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%0 Conference Proceedings
%T Arabic Sentiment Analysis by Pretrained Ensemble
%A Qaddoumi, Abdelrahim
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F qaddoumi-2022-arabic
%X This paper presents the 259 team’s BERT ensemble designed for the NADI 2022 Subtask 2 (sentiment analysis) (Abdul-Mageed et al., 2022). Twitter Sentiment analysis is one of the language processing (NLP) tasks that provides a method to understand the perception and emotions of the public around specific topics. The most common research approach focuses on obtaining the tweet’s sentiment by analyzing its lexical and syntactic features. We used multiple pretrained Arabic-Bert models with a simple average ensembling and then chose the best-performing ensemble on the training dataset and ran it on the development dataset. This system ranked 3rd in Subtask 2 with a Macro-PN-F1-score of 72.49%.
%R 10.18653/v1/2022.wanlp-1.47
%U https://aclanthology.org/2022.wanlp-1.47
%U https://doi.org/10.18653/v1/2022.wanlp-1.47
%P 447-451
Markdown (Informal)
[Arabic Sentiment Analysis by Pretrained Ensemble](https://aclanthology.org/2022.wanlp-1.47) (Qaddoumi, WANLP 2022)
ACL
- Abdelrahim Qaddoumi. 2022. Arabic Sentiment Analysis by Pretrained Ensemble. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 447–451, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.