UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis

• URL: http://arxiv.org/abs/2304.11256v2
• Date: Tue, 25 Apr 2023 07:44:37 GMT
• Title: UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis
• Authors: Gagan Bhatia, Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed
• Abstract summary: We tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual models under a full supervised setting. Our results demonstrate the effectiveness of transfer learning and fine-tuning techniques for sentiment analysis.
• Score: 5.945320097465418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where we tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual models under a full supervised setting (subtasks A and B). We also develop models for the zero-shot setting (subtask C). Our approach involves experimenting with transfer learning using six language models, including further pertaining of some of these models as well as a final finetuning stage. Our best performing models achieve an F1-score of 70.36 on development data and an F1-score of 66.13 on test data. Unsurprisingly, our results demonstrate the effectiveness of transfer learning and fine-tuning techniques for sentiment analysis across multiple languages. Our approach can be applied to other sentiment analysis tasks in different languages and domains.

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