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Nigerian English adaptation, Pidgin, has evolved over the years throughmulti-language code switching, code mixing and linguistic adaptation. WhilePidgin preserves many of the words in the normal English language corpus, bothin spelling and pronunciation, the fundamental meaning of these words havechanged significantly. For example,'ginger' is not a plant but an expression ofmotivation and 'tank' is not a container but an expression of gratitude. Theimplication is that the current approach of using direct English sentimentanalysis of social media text from Nigeria is sub-optimal, as it will not beable to capture the semantic variation and contextual evolution in thecontemporary meaning of these words. In practice, while many words in NigerianPidgin adaptation are the same as the standard English, the full Englishlanguage based sentiment analysis models are not designed to capture the fullintent of the Nigerian pidgin when used alone or code-mixed. By augmentingscarce human labelled code-changed text with ample synthetic code-reformattedtext and meaning, we achieve significant improvements in sentiment scoring. Ourresearch explores how to understand sentiment in an intrasentential code mixingand switching context where there has been significant word localization.Thiswork presents a 300 VADER lexicon compatible Nigerian Pidgin sentiment tokensand their scores and a 14,000 gold standard Nigerian Pidgin tweets and theirsentiments labels.

Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification