NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
This addresses the problem of low-resource language sentiment analysis for researchers and practitioners in NLP, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of sentiment analysis data for Nigerian languages by creating NaijaSenti, a large-scale annotated Twitter dataset covering Hausa, Igbo, Nigerian-Pidgin, and Yorùbá with around 30,000 tweets per language (14,000 for Nigerian-Pidgin), and found that language-specific models and adaptive fine-tuning performed best.
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 (and 14,000 for Nigerian-Pidgin), 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 rangeof pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptivefine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivizeresearch on sentiment analysis in under-represented languages.