Version 1
: Received: 3 September 2024 / Approved: 3 September 2024 / Online: 3 September 2024 (18:02:13 CEST)
How to cite:
Mabokela, R.; Primus, M.; Celik, T. Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages. Preprints2024, 2024090253. https://doi.org/10.20944/preprints202409.0253.v1
Mabokela, R.; Primus, M.; Celik, T. Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages. Preprints 2024, 2024090253. https://doi.org/10.20944/preprints202409.0253.v1
Mabokela, R.; Primus, M.; Celik, T. Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages. Preprints2024, 2024090253. https://doi.org/10.20944/preprints202409.0253.v1
APA Style
Mabokela, R., Primus, M., & Celik, T. (2024). Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages. Preprints. https://doi.org/10.20944/preprints202409.0253.v1
Chicago/Turabian Style
Mabokela, R., Mpho Primus and Turgay Celik. 2024 "Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages" Preprints. https://doi.org/10.20944/preprints202409.0253.v1
Abstract
Sentiment analysis is a pivotal tool for gauging the public’s perception and understanding of human communication across digital social media platforms. However, due to linguistic complexities and limited resources, sentiment analysis is not well-represented in many African languages. While benchmark Africa-Centric Pre-trained Language Models (PLMs) have been developed for various Natural Language Processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain unexplored. In this study, we introduce a novel approach that combines Africa-centric PLMs with XAI techniques for sentiment analysis. We demonstrate that applying attention mechanisms and visualisation techniques improves the transformer-based model’s transparency, trustworthiness, and decision-making abilities when making sentiment predictions. We then employ the SAfriSenti—a multilingual sentiment corpus for South African under-resourced languages. We use the corpus to perform various sentiment analysis experiments and also enable comprehensive evaluations, comparing the performance of Africa-centric models against mainstream PLMs. The Afro-XLMR model outperformed all models and achieved an average F1-score performance of 71.04% across the five tested languages and the lowest error rate among the evaluated models. Additionally, we incorporated techniques like Local Interpretive Model-Agnostic Interpretation (LIME) and Shapley Additive Interpretation (SHAP) in the sentiment classifier’s output to enhance the Afro-XLMR model’s interpretability and explainability. As a result, the use of XAI strategies ensures that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in the decision-making of AI-driven NLP technologies, particularly in the context of African languages.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
