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Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work. 

Sentiment Analysis is a popular text classification task in natural language processing. It involves developing algorithms or machine learning models to determine the sentiment or opinion expressed in a piece of text. The results of this task can be used by business owners and product developers to understand their consumers’ perceptions of their products. Asides from customer feedback and product/service analysis, this task can be useful for social media monitoring (Martin et al., 2021). One of the popular applications of sentiment analysis is for classifying and detecting the positive and negative sentiments on movie reviews. Movie reviews enable movie producers to monitor the performances of their movies (Abhishek et al., 2020) and enhance the decision of movie viewers to know whether a movie is good enough and worth investing time to watch (Lakshmi Devi et al., 2020). However, the task has been under-explored for African languages compared to their western counterparts, ”high resource languages”, that are privileged to have received enormous attention due to the large amount of available textual data. African languages fall under the category of the low resource languages which are on the disadvantaged end because of the limited availability of data that gives them a poor representation (Nasim & Ghani, 2020). Recently, sentiment analysis has received attention on African languages in the Twitter domain for Nigerian (Muhammad et al., 2022) and Amharic (Yimam et al., 2020) languages. However, there is no available corpus in the movie domain. We decided to tackle the problem of unavailability of Yoru`ba´ data for movie sentiment analysis by creating the first Yoru`ba´ sentiment corpus for Nollywood movie reviews. Also, we develop sentiment classification models using state-of-the-art pre-trained language models like mBERT (Devlin et al., 2019) and AfriBERTa (Ogueji et al., 2021).