Nollywood, based on the idea of Bollywood from India, is a series of
outstanding movies that originate from Nigeria. Unfortunately, while the movies
are in English, they are hard to understand for many native speakers due to the
dialect of English that is spoken. In this article, we accomplish two goals:
(1) create a phonetic sub-title model that is able to translate Nigerian
English speech to American English and (2) use the most advanced toxicity
detectors to discover how toxic the speech is. Our aim is to highlight the text
in these videos which is often times ignored for lack of dialectal
understanding due the fact that many people in Nigeria speak a native language
like Hausa at home.

本文旨在创建一个能够将尼日利亚英语口语译为美式英语的音标字幕模型，并使用最先进的毒性检测器来发现语音的毒性程度，以强调这些视频中经常被忽视的文本，因为尼日利亚许多人在家里使用像豪萨语这样的母语，导致缺乏方言理解。

Nollywood：电影院见！

Nollywood: Let's Go to the Movies!

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.

本研究探讨了如何在 Pidgin 语境下理解情感分析，提出了一个 300 词汇表和 14,000 条推文的 Nigerian Pidgin 情感标签数据集，指出了将传统英语情感分析模型直接用于 Nigeria Pidgin 文本的缺陷。