Mayank Kumar


2023

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Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset
Alina Hancharova | John Wang | Mayank Kumar
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper presents a study on the effectiveness of various approaches for addressing the challenge of multilingual sentiment analysis in low-resource African languages. . The approaches evaluated in the study include Support Vector Machines (SVM), translation, and an ensemble of pre-trained multilingual sentimental models methods. The paper provides a detailed analysis of the performance of each approach based on experimental results. In our findings, we suggest that the ensemble method is the most effective with an F1-Score of 0.68 on the final testing. This system ranked 19 out of 33 participants in the competition.