@inproceedings{salahudeen-etal-2023-hausanlp,
title = "{H}ausa{NLP} at {S}em{E}val-2023 Task 12: Leveraging {A}frican Low Resource {T}weet{D}ata for Sentiment Analysis",
author = "Salahudeen, Saheed Abdullahi and
Lawan, Falalu Ibrahim and
Wali, Ahmad and
Imam, Amina Abubakar and
Shuaibu, Aliyu Rabiu and
Yusuf, Aliyu and
Rabiu, Nur Bala and
Bello, Musa and
Adamu, Shamsuddeen Umaru and
Aliyu, Saminu Mohammad",
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-1.6/",
doi = "10.18653/v1/2023.semeval-1.6",
pages = "50--57",
abstract = "We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment classification using the tracks in subtask A and subtask C is a zero-shot sentiment classification. We present the results and findings of subtask A, subtask B and subtask C. We also release the code on github. Our goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large, AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert), Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African languages. The datasets for these subtasks consists of a gold standard multi-class labeled Twitter datasets from these languages. Our results demonstrate that Afro-xlmr-large model performed better compared to the other models in most of the languages datasets. Similarly, Nigerian languages: Hausa, Igbo, and Yoruba achieved better performance compared to other languages and this can be attributed to the higher volume of data present in the languages."
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<abstract>We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment classification using the tracks in subtask A and subtask C is a zero-shot sentiment classification. We present the results and findings of subtask A, subtask B and subtask C. We also release the code on github. Our goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large, AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert), Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African languages. The datasets for these subtasks consists of a gold standard multi-class labeled Twitter datasets from these languages. Our results demonstrate that Afro-xlmr-large model performed better compared to the other models in most of the languages datasets. Similarly, Nigerian languages: Hausa, Igbo, and Yoruba achieved better performance compared to other languages and this can be attributed to the higher volume of data present in the languages.</abstract>
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%0 Conference Proceedings
%T HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis
%A Salahudeen, Saheed Abdullahi
%A Lawan, Falalu Ibrahim
%A Wali, Ahmad
%A Imam, Amina Abubakar
%A Shuaibu, Aliyu Rabiu
%A Yusuf, Aliyu
%A Rabiu, Nur Bala
%A Bello, Musa
%A Adamu, Shamsuddeen Umaru
%A Aliyu, Saminu Mohammad
%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 salahudeen-etal-2023-hausanlp
%X We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment classification using the tracks in subtask A and subtask C is a zero-shot sentiment classification. We present the results and findings of subtask A, subtask B and subtask C. We also release the code on github. Our goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large, AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert), Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African languages. The datasets for these subtasks consists of a gold standard multi-class labeled Twitter datasets from these languages. Our results demonstrate that Afro-xlmr-large model performed better compared to the other models in most of the languages datasets. Similarly, Nigerian languages: Hausa, Igbo, and Yoruba achieved better performance compared to other languages and this can be attributed to the higher volume of data present in the languages.
%R 10.18653/v1/2023.semeval-1.6
%U https://aclanthology.org/2023.semeval-1.6/
%U https://doi.org/10.18653/v1/2023.semeval-1.6
%P 50-57
Markdown (Informal)
[HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis](https://aclanthology.org/2023.semeval-1.6/) (Salahudeen et al., SemEval 2023)
ACL
- Saheed Abdullahi Salahudeen, Falalu Ibrahim Lawan, Ahmad Wali, Amina Abubakar Imam, Aliyu Rabiu Shuaibu, Aliyu Yusuf, Nur Bala Rabiu, Musa Bello, Shamsuddeen Umaru Adamu, and Saminu Mohammad Aliyu. 2023. HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 50–57, Toronto, Canada. Association for Computational Linguistics.