  Africa has over 2000 indigenous languages but they are under-represented in
NLP research due to lack of datasets. In recent years, there have been progress
in developing labeled corpora for African languages. However, they are often
available in a single domain and may not generalize to other domains. In this
paper, we focus on the task of sentiment classification for cross domain
adaptation. We create a new dataset, NollySenti - based on the Nollywood movie
reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo,
Nigerian-Pidgin, and Yoruba. We provide an extensive empirical evaluation using
classical machine learning methods and pre-trained language models. Leveraging
transfer learning, we compare the performance of cross-domain adaptation from
Twitter domain, and cross-lingual adaptation from English language. Our
evaluation shows that transfer from English in the same target domain leads to
more than 5% improvement in accuracy compared to transfer from Twitter in the
same language. To further mitigate the domain difference, we leverage machine
translation (MT) from English to other Nigerian languages, which leads to a
further improvement of 7% over cross-lingual evaluation. While MT to
low-resource languages are often of low quality, through human evaluation, we
show that most of the translated sentences preserve the sentiment of the
original English reviews.
