Arabic Sentiment Analysis by Pretrained Ensemble

Abdelrahim Qaddoumi

doi:10.18653/v1/2022.wanlp-1.47

Arabic Sentiment Analysis by Pretrained Ensemble

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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%.

Anthology ID:: 2022.wanlp-1.47
Volume:: Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
Month:: December
Year:: 2022
Address:: Abu Dhabi, United Arab Emirates (Hybrid)
Editors:: Houda Bouamor, Hend Al-Khalifa, Kareem Darwish, Owen Rambow, Fethi Bougares, Ahmed Abdelali, Nadi Tomeh, Salam Khalifa, Wajdi Zaghouani
Venue:: WANLP
SIG:: SIGARAB
Publisher:: Association for Computational Linguistics
Note:
Pages:: 447–451
Language:
URL:: https://aclanthology.org/2022.wanlp-1.47/
DOI:: 10.18653/v1/2022.wanlp-1.47
Bibkey:
Cite (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.
Cite (Informal):: Arabic Sentiment Analysis by Pretrained Ensemble (Qaddoumi, WANLP 2022)
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PDF:: https://aclanthology.org/2022.wanlp-1.47.pdf

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@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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%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

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Markdown (Informal)

[Arabic Sentiment Analysis by Pretrained Ensemble](https://aclanthology.org/2022.wanlp-1.47/) (Qaddoumi, WANLP 2022)

Arabic Sentiment Analysis by Pretrained Ensemble (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.