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Sentiment Analysis and Language Models for Kwanyama

Summary

Kwanyama is a Bantu language spoken by roughly 1.5 million people in northern Namibia and southern Angola. Before this work it had almost no computational resources: no embeddings, no pretrained models, and no labeled or raw corpora. The paper fills this gap with two openly released resources.

OkaLM is a family of Kwanyama models at 1B, 3B and 8B parameters. They were built by continuing to pretrain LLaMA-3 checkpoints for a single epoch on a cleaned 6.58M-token corpus. Most of that corpus is religious text from jw.org, supplemented by government documents and a little news.

OkaSentiment is a three-class sentiment dataset. Earlier African sentiment sets mostly came from Twitter, but this one consists of reviews of domestic-work relationships, such as housekeepers, herders and nannies. Screened university students wrote the reviews after being primed with a polarity and an aspect, which countered an early drift toward repetitive, positive text.

Text quality was checked through perplexity, lexical statistics and expert judgment, and 14 open and proprietary models were benchmarked. The provided excerpt omits the benchmark scores. The work gives Kwanyama its first benchmark and language models.

Key points

  • OkaSentiment has 1,127 reviews (19,927 tokens), averaging 18 words and at most 80 words per review.
    • Train: 827 reviews (313 negative / 313 positive / 201 neutral).
    • Test: 300 reviews (100 per class).
  • Reviews span 11 aspects: being honest, behaving decently, keeping things clean, how children are cared for, preparing food, being on time, showing respect, paying on time, communication, trustworthiness, and whether the worker is recommended overall.
  • Annotation process:
    • 65 students took a writing test and 46 passed; one contributor's output was later discarded.
    • Reviews written in the closely related Ndonga dialect were kept.
    • Collection took one month and cost about USD 2,500.
  • OkaLM training setup:
    • 4×A40 GPUs, fp16, peak LR 1e-5 with a cosine schedule and 2,500 warmup steps.
    • Default LLaMA-3 tokenizer and HuggingFace Transformers.
    • Training beyond one epoch caused overfitting.
    • Total cost was 20.24 GPU hours, an estimated 8.12 kg CO₂.
  • All resources are published at github.com/okalai-ai/okaResource.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Language models & pretraining · Sentiment & emotion

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4whctbu5acfp

@inproceedings{nakashole2026sentiment,
  title = {{Sentiment Analysis and Language Models for Kwanyama}},
  author = {Nakashole, Ndapa},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4whctbu5acfp},
  url = {https://doi.org/10.63317/4whctbu5acfp}
}

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