Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset
Shashank Rathi, Siddhesh Pande, Harshwardhan Atkare, Rahul Tangsali, Aditya Vyawahare, Dipali Kadam
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 12: afrisenti-semeval: sentiment analysis for low-resource african languages using twitter dataset Paper
TLDR:
In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbal
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Abstract:
In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.