# Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification

arXiv: https://arxiv.org/abs/2003.12450 · HTML: https://mlsanity.com/paper/2003.12450/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [On Language Models for Creoles](https://mlsanity.com/paper/2109.06074/) (2021)
- [Krey`ol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages](https://mlsanity.com/paper/2405.05376/) (2024)
- [Where Are We? Evaluating LLM Performance on African Languages](https://mlsanity.com/paper/2502.19582/) (2025)
- [CreoleVal: Multilingual Multitask Benchmarks for Creoles](https://mlsanity.com/paper/2310.19567/) (2023)
- [Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin](https://mlsanity.com/paper/2307.00382/) (2023)
- [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://mlsanity.com/paper/2310.14557/) (2023)
- [Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin](https://mlsanity.com/paper/2404.18264/) (2024)
- [UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis](https://mlsanity.com/paper/2304.11256/) (2023)
- [Limited-Resource Adapters Are Regularizers, Not Linguists](https://mlsanity.com/paper/2505.24525/) (2025)

## Abstract (arXiv)

Nigerian English adaptation, Pidgin, has evolved over the years through multi-language code switching, code mixing and linguistic adaptation. While Pidgin preserves many of the words in the normal English language corpus, both in spelling and pronunciation, the fundamental meaning of these words have changed significantly. For example,'ginger' is not a plant but an expression of motivation and 'tank' is not a container but an expression of gratitude. The implication is that the current approach of using direct English sentiment analysis of social media text from Nigeria is sub-optimal, as it will not be able to capture the semantic variation and contextual evolution in the contemporary meaning of these words. In practice, while many words in Nigerian Pidgin adaptation are the same as the standard English, the full English language based sentiment analysis models are not designed to capture the full intent of the Nigerian pidgin when used alone or code-mixed. By augmenting scarce human labelled code-changed text with ample synthetic code-reformatted text and meaning, we achieve significant improvements in sentiment scoring. Our research explores how to understand sentiment in an intrasentential code mixing and switching context where there has been significant word localization.This work presents a 300 VADER lexicon compatible Nigerian Pidgin sentiment tokens and their scores and a 14,000 gold standard Nigerian Pidgin tweets and their sentiments labels.