Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, I. Ahmad, Idris Abdulmumin, Bello Shehu Bello, M. Choudhury, Chris C. Emezue, Anuoluwapo Aremu, Saheed Abdul, P. Brazdil

International Conference on Language Resources and Evaluation

Paper

Abstract

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 Yorùbá—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.