NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Muhammad, SH (Author) Other; Adelani, DI (Author) Other; Ruder, S (Author) Other; Ahmad, IS (Author) Other; Abdulmumin, I (Author) Other; Bello, BS (Author) Other; Choudhury, M (Author) Other; Emezue, CC (Author) Other; Abdullahi, SS (Author) Other; Aremu, A (Author) Other; Jorge, AM (Author) FCUP; Pavel Brazdil (Author) FEP

Conference proceedings International

Title: Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)

Pages: 590-602

LREC 2022: Language Resources and Evaluation Conference, 13

Marseille, 2022

Indexing

ISI Web of Knowledge - 0 Citations

Other information

Authenticus ID: P-00W-5M1

Abstract (EN): Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria-Hausa, Igbo, Nigerian-Pidgin, and Yoruba-consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing, and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13