Background
Language is evolving with the flattening world order and the pervasiveness of the social media in fusing culture and bridging relationships at a click. One of the consequences of the conversational evolution is the intrasentential code switching, a language alternation in a single discourse between two languages, where the switching occurs within a sentence BIBREF0. The increased instances of these often lead to changes in the lexical and grammatical context of the language, which are largely motivated by situational and stylistic factors BIBREF1. In addition, the need to communicate effectively to different social classes have further orchestrated this shift in language meaning over a long period of time to serve socio-linguistic functions BIBREF2 Nigeria is estimated to have between three and five million people, who primarily use Pidgin in their day-to-day interactions. But it is said to be a second language to a much higher number of up to 75 million people in Nigeria alone, about half the population.BIBREF3. It has evolved in meaning compared to Standard English due to intertextuality, the shaping of a text's meaning by another text based on the interconnection and influence of the audience's interpretation of a text. One of the biggest social catalysts is the emerging urban youth subculture and the new growing semi-literate lower class in a chaotic medley of a converging megacity BIBREF4 BIBREF5 VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media and works well on texts from other domains. VADER lexicon has about 9000 tokens (built from existing well-established sentiment word-banks (LIWC, ANEW, and GI) incorporated with a full list of Western-style emoticons, sentiment-related acronyms and initialisms (e.g., LOL and WTF)commonly used slang with sentiment value (e.g., nah, meh and giggly) ) with their mean sentiment rating.BIBREF6. Sentiment analysis in code-mixed text has been established in literature both at word and sub-word levels BIBREF7 BIBREF8 BIBREF9. The possibility of improving sentiment detection via label transfer from monolingual to synthetic code-switched text has been well executed with significant improvements in sentiment labelling accuracy (1.5%, 5.11%, 7.20%) for three different language pairs BIBREF5
Method
This study uses the original and updated VADER (Valence Aware Dictionary and Sentiment Reasoner) to calculate the compound sentiment scores for about 14,000 Nigerian Pidgin tweets. The updated VADER lexicon (updated with 300 Pidgin tokens and their sentiment scores) performed better than the original VADER lexicon. The labelled sentiments from the updated VADER were then compared with sentiment labels by expert Pidgin English speakers.
Results
During the translation of VADER English lexicon to suitable one-word Nigerian Pidgin translation, a total of 300 Nigerian pidgin tokens were successfully translated from the standard VADER English lexicon. One of the challenges of this translation is that the direct translation of most the sentiment words in the original VADER English Lexicon translates to phrases not single one-word tokens and certain pidgin words translates to many english words.TABREF5.
Conclusion
The quality of sentiment labels generated by our updated VADER lexicon is better compared to the labels generated by the original VADER English lexicon.TABREF4.Sentiment labels by human annotators was able to capture nuances that the rule based sentiment labelling could not capture.More work can be done to increase the number of instances in the dataset.
Appendix ::: Selection of Data Labellers
Three people who are indigenes or lived in the South South part of Nigeria, where Nigerian Pidgin is a prevalent method of communication were briefed on the fundamentals of word sentiments. Each labelled Data point was verified by at least one other person after initial labelling.
Appendix ::: Selection of Data Labellers ::: Acknowledgments
We acknowledge Kessiena Rita David,Patrick Ehizokhale Oseghale and Peter Chimaobi Onuoha for using their mastery of Nigerian Pidgin to translate and label the datasets.

Question: How many annotators were used for sentiment labeling?
Answer: Each labelled Data point was verified by at least one other person after initial labelling.

Three people

Question: How is data collected?
Answer: original and updated VADER (Valence Aware Dictionary and Sentiment Reasoner)

Unanswerable

Question: How much better is performance of Nigerian Pitdgin English sentiment classification of models that use additional Nigerian English data compared to orginal English-only models?
Answer: Unanswerable

Unanswerable

Question: What full English language based sentiment analysis models are tried?
Answer: the original VADER English lexicon.

Unanswerable