Team ISCL_WINTER at SemEval-2023 Task 12:AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset

Abstract

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 study focuses on Task 12 of the SemEval-2023 Competition, which aims to promote interest in these languages and develop efficient models for their analysis. The approaches evaluated in the study include Support Vector Machines (SVM), translation, and an ensemble of pre-trained multilingual sentimental model 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.