yosm: A new yoruba sentiment corpus for movie reviews
This addresses the problem of limited datasets for African languages like Yoruba, enabling sentiment analysis in a domain-specific context, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of sentiment analysis resources for low-resource languages by creating a new Yoruba sentiment corpus of 1500 movie reviews, achieving classification using pre-trained models like mBERT and AfriBERTa.
A movie that is thoroughly enjoyed and recommended by an individual might be hated by another. One characteristic of humans is the ability to have feelings which could be positive or negative. To automatically classify and study human feelings, an aspect of natural language processing, sentiment analysis and opinion mining were designed to understand human feelings regarding several issues which could affect a product, a social media platforms, government, or societal discussions or even movies. Several works on sentiment analysis have been done on high resource languages while low resources languages like Yoruba have been sidelined. Due to the scarcity of datasets and linguistic architectures that will suit low resource languages, African languages "low resource languages" have been ignored and not fully explored. For this reason, our attention is placed on Yoruba to explore sentiment analysis on reviews of Nigerian movies. The data comprised 1500 movie reviews that were sourced from IMDB, Rotten Tomatoes, Letterboxd, Cinemapointer and Nollyrated. We develop sentiment classification models using the state-of-the-art pre-trained language models like mBERT and AfriBERTa to classify the movie reviews.