  The evolution of the Internet has increased the amount of information that is
expressed by people on different platforms. This information can be product
reviews, discussions on forums, or social media platforms. Accessibility of
these opinions and peoples feelings open the door to opinion mining and
sentiment analysis. As language and speech technologies become more advanced,
many languages have been used and the best models have been obtained. However,
due to linguistic diversity and lack of datasets, African languages have been
left behind. In this study, by using the current state-of-the-art model,
multilingual BERT, we perform sentiment classification on Swahili datasets. The
data was created by extracting and annotating 8.2k reviews and comments on
different social media platforms and the ISEAR emotion dataset. The data were
classified as either positive or negative. The model was fine-tuned and achieve
the best accuracy of 87.59%.
