# Sentiment Classification in Swahili Language Using Multilingual BERT

arXiv: https://arxiv.org/abs/2104.09006 · HTML: https://mlsanity.com/paper/2104.09006/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Sentiment Analysis Based on Deep Learning: A Comparative Study](https://mlsanity.com/paper/2006.03541/) (2020)
- [COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification](https://mlsanity.com/paper/2005.10898/) (2020)
- [Learning Word Representations for Tunisian Sentiment Analysis](https://mlsanity.com/paper/2010.06857/) (2020)

## Built on by (most cited first, 6 of 6)

- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [yosm: A new yoruba sentiment corpus for movie reviews](https://mlsanity.com/paper/2204.09711/) (2022)
- [Continually Adding New Languages to Multilingual Language Models](https://mlsanity.com/paper/2509.11414/) (2025)
- [GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters](https://mlsanity.com/paper/2304.12979/) (2023)
- [Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model](https://mlsanity.com/paper/2404.09045/) (2024)
- [Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach](https://mlsanity.com/paper/2508.14051/) (2025)

## Abstract (arXiv)

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%.