---
title: "Semantic Enrichment of Nigerian Pidgin English for Contextual"
description: "The work matters because it releases two resources for an under-served, widely spoken contact… 10 references, cited by 16 papers here."
authors: ["Wuraola Fisayo Oyewusi", "Olubayo Adekanmbi", "Olalekan Akinsande"]
year: 2020
venue: "arXiv:2003.12450"
source: "https://arxiv.org/abs/2003.12450"
doi: "10.48550/arxiv.2003.12450"
arxiv: "2003.12450"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/oyewusi-2020-semantic-enrichment-nigerian-pidgin-english/"
---
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# Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification

**Authors:** Wuraola Fisayo Oyewusi, Olubayo Adekanmbi and Olalekan Akinsande · **Year:** 2020 · **Venue:** arXiv:2003.12450 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2003.12450) · [PDF](https://arxiv.org/pdf/2003.12450) · [DOI 10.48550/arxiv.2003.12450](https://doi.org/10.48550/arxiv.2003.12450) · [arXiv 2003.12450](https://arxiv.org/abs/2003.12450) · [OpenAlex](https://openalex.org/W3013055388)

**Citations:** 15 citations (OpenAlex, 2026-10-14) · [16](https://md.research.elamur.ai/free/en/papers/oyewusi-2020-semantic-enrichment-nigerian-pidgin-english/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Social media text from Nigeria often mixes Standard English with Nigerian Pidgin, within a single sentence. Many Pidgin words look like English but carry different meanings: "ginger" signals motivation, and "tank" expresses thanks. Off-the-shelf English sentiment tools therefore misjudge such text. The authors adapt VADER, a rule-based, lexicon-driven sentiment scorer built for social media. Fluent Pidgin speakers translated entries of the English VADER lexicon into one-word Pidgin equivalents, which yielded 300 Pidgin tokens with VADER-style scores. The original and the extended lexicon were then each used to label roughly 14,000 Pidgin tweets, and their outputs were compared with labels from expert Pidgin speakers. The extended lexicon produced labels closer to the human judgments. Sample tweets show neutral or wrong-polarity scores flipping to the correct polarity. Humans still caught nuances that the rules missed. The work matters because it releases two resources for an under-served, widely spoken contact language: a VADER-compatible Pidgin sentiment lexicon and a gold-labelled tweet corpus.

## Key points
- Released resources: 300 Pidgin sentiment tokens with scores that plug into VADER's roughly 9,000-entry lexicon, and about 14,000 Nigerian Pidgin tweets with gold sentiment labels.
- Nigerian Pidgin is the primary language of an estimated 3–5 million people and a second language for up to 75 million Nigerians.
- Most English sentiment words translate into Pidgin phrases rather than single tokens, which limited lexicon coverage. Some Pidgin words, such as kasala, gbege and para, correspond to several English entries, so their scores were averaged.
- Three annotators from Nigeria's South South region labelled the data, and at least one other person checked each label.
- Evaluation is qualitative: a table of example tweets shows compound scores before and after the update alongside the expert label. No aggregate accuracy figure is reported.

## Interesting ideas
- When one dialect word corresponds to several entries in a source lexicon, giving it the mean of their scores is a cheap way to inherit values without new annotation.
- Look-alike words with shifted meanings ("false friends") are fixed by layering a small dialect overlay on top of an existing lexicon, not by retraining.
- The evaluation lines up three judgments per sentence: the old system, the updated system, and a human expert. This makes every flip visible and attributable to the lexicon change.
- Each label is created by one annotator and confirmed by a second, which is a low-cost quality gate for gold data in a niche variety.
- Translating a lexicon showed that many concepts surface as multiword phrases in the target variety. Restricting to single tokens loses coverage.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): compiled value or polarity tables could assign dialect or slang lemmas the averaged values of their WordNet-linked standard synonyms, with the source entries recorded for explainability.
- **I2** (tokens): a register- or dialect-keyed lemma/sense overlay in crate en could redirect forms like "tank" to their local sense before tagging.
- **I3** (learn): rule or lexicon changes could be evaluated as per-sentence before/after diffs against gold, so each fix and each regression can be explained.
- **I4** (learn): small gold sets that check silver data from the witness parsers could use the same two-person check.
- **I5** (frames): sentiment- or attitude-bearing expressions in prag could be stored as multiword entries matched on the UD tree, not as single tokens.

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

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,888 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 798 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 463 works

🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works · [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 609 works

## Cite

Cite as: Wuraola Fisayo Oyewusi, Olubayo Adekanmbi and Olalekan Akinsande. 2020. Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification. arXiv:2003.12450.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{oyewusi2020semantic,
  title = {{Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification}},
  author = {Wuraola Fisayo Oyewusi, Olubayo Adekanmbi and Olalekan Akinsande},
  year = {2020},
  doi = {10.48550/arxiv.2003.12450},
  eprint = {2003.12450},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2003.12450}
}
```

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## References

10 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Tosin Carons, Abraham and M Amaka Onyioha. The origin of pidgin. Afrostyle Magazine, 2, 2012. URL [http://www.afrostylemag.com/ASM7/pidgin.html](http://www.afrostylemag.com/ASM7/pidgin.html). [↗](http://www.afrostylemag.com/ASM7/pidgin.html)
2. C.J Hutto and Eric Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. Eighth International Conference on Weblogs and Social Media (ICWSM-14), 2014. [find](https://scholar.google.com/scholar?q=C%2EJ+Hutto+and+Eric+Gilbert%2E+Vader%3A+A+parsimonious+rule%2Dbased+model+for+sentiment+analysis+of+social+media+text%2E+Eighth+International+Conference+on+Weblogs+and+Social+Media+%28ICWSM%2D14%29%2C+2014%2E)
3. Jane Ifechelobi. Code switching: a variation in language use. Mgbakoigba: Journal of African Studies, 4:1–7, 2015. [find](https://scholar.google.com/scholar?q=Jane+Ifechelobi%2E+Code+switching%3A+a+variation+in+language+use%2E+Mgbakoigba%3A+Journal+of+African+Studies%2C+4%3A1%E2%80%937%2C+2015%2E)
4. Herbert Igboanusi. Empowering nigerian pidgin: A challenge for status planning? World Englishes, 27:68 – 82, 02 2008. doi: 10.1111/j.1467-971X.2008.00536.x. [↗](https://doi.org/10.1111/j.1467-971x.2008.00536.x)
5. Sentiment Label before VADER English Lexicon Update Sentiment Label after VADER English Lexicon Update Sentiment Label by Expert Pidgin Speaker Compound Sentiment Score after VADER English Lexicon Update 0.7964 negative positive positive 0.5080 neutral positive positive 0.6209 neutral positive positive 0.5106 neutral positive positive -0.6908 0.5423 neutral positive negative positive negative positive -0.2500 positive negative negative Yusuf Inuwa, Nuhu, Anne Christopher, Althea, and Haryati Bakrin, Bt. Factors motivating code switching within the social contact of hausa bilinguals. IOSR Journal Of Humanities And Social Science (IOSR-JHSS), 19:43 – 49, 2014. doi: 10.1016/j.sbspro.2013.01.173. [↗](https://doi.org/10.1016/j.sbspro.2013.01.173)
6. Hayeon Jang and Hyopil Shin. Language-specific sentiment analysis in morphologically rich languages. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 498–506. Association for Computational Linguistics, 2010. [find](https://scholar.google.com/scholar?q=Hayeon+Jang+and+Hyopil+Shin%2E+Language%2Dspecific+sentiment+analysis+in+morphologically+rich+languages%2E+In+Proceedings+of+the+23rd+International+Conference+on+Computational+Linguistics%3A+Posters%2C+pp%2E+498%E2%80%93)
7. Didem Koban. Intra-sentential and inter-sentential code-switching in turkish-english bilinguals in new york city, u.s. Procedia - Social and Behavioral Sciences, 70:1174–1179, 01 2013. doi: 10.1016/j.sbspro.2013.01.173. [↗](https://doi.org/10.1016/j.sbspro.2013.01.173)
8. Ameya Prabhu, Aditya Joshi, Manish Shrivastava, and Vasudeva Varma. Towards sub-word level compositions for sentiment analysis of hindi-english code mixed text. 11 2016. [↗](https://doi.org/10.48550/arxiv.1611.00472)
9. Iñaki San Vicente Roncal. Multilingual sentiment analysis in social media. PhD thesis, Universidad del Paı́s Vasco-Euskal Herriko Unibertsitatea, 2019. [find](https://scholar.google.com/scholar?q=In%CC%83aki+San+Vicente+Roncal%2E+Multilingual+sentiment+analysis+in+social+media%2E+PhD+thesis%2C+Universidad+del+Pa%C4%B1%CC%81s+Vasco%2DEuskal+Herriko+Unibertsitatea%2C+2019%2E)
10. Bidisha Samanta, Niloy Ganguly, and Soumen Chakrabarti. Improved sentiment detection via label transfer from monolingual to synthetic code-switched text. arXiv preprint arXiv:1906.05725, 2019. [↗](https://arxiv.org/abs/1906.05725)

*Updated 24 October 2026*