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Africa has over 2000 indigenous languages but they are under-represented inNLP research due to lack of datasets. In recent years, there have been progressin developing labeled corpora for African languages. However, they are oftenavailable in a single domain and may not generalize to other domains. In thispaper, we focus on the task of sentiment classification for cross domainadaptation. We create a new dataset, NollySenti - based on the Nollywood moviereviews for five languages widely spoken in Nigeria (English, Hausa, Igbo,Nigerian-Pidgin, and Yoruba. We provide an extensive empirical evaluation usingclassical machine learning methods and pre-trained language models. Leveragingtransfer learning, we compare the performance of cross-domain adaptation fromTwitter domain, and cross-lingual adaptation from English language. Ourevaluation shows that transfer from English in the same target domain leads tomore than 5% improvement in accuracy compared to transfer from Twitter in thesame language. To further mitigate the domain difference, we leverage machinetranslation (MT) from English to other Nigerian languages, which leads to afurther improvement of 7% over cross-lingual evaluation. While MT tolow-resource languages are often of low quality, through human evaluation, weshow that most of the translated sentences preserve the sentiment of theoriginal English reviews.

NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification