| Abstrakt: |
An aspect-based sentiment analysis (ABSA) aims to perform a fine-grained analysis of text to identify sentiments and opinions associated with specific aspects. Recently, transformers and large language models have demonstrated exceptional performance in detecting aspects and determining their associated sentiments within text. However, understanding the decision-making processes of transformers remains a significant challenge, as they often operate as black-box models, making it difficult to interpret how they arrive at specific predictions. In this article, we examine the performance of various transformers on ABSA and we employ explainability techniques to illustrate their inner decision-making processes. Firstly, we fine-tune several pre-trained transformers, including BERT, RoBERTa, DistilBERT, and XLNet, on an extensive set of data composed of MAMS, SemEval, and Naver datasets. These datasets consist of over 16,100 complex sentences, each containing a couple of aspects and corresponding polarities. The models were fine-tuned using optimal hyperparameters and RoBERTa achieved the highest performance, reporting 89.16% accuracy on MAMS and SemEval and 97.62% on Naver. We implemented five explainability techniques, LIME, SHAP, attention weight visualization, integrated gradients, and Grad-CAM, to illustrate how transformers make predictions and highlight influential words. These techniques can reveal how models use specific words and contextual information to make sentiment predictions, which can improve performance, address biases, and enhance model efficiency and robustness. These also point out directions for further focus on the analysis of models' bias in combination with explainability methods, ensuring that explainability highlights potential biases in predictions. [ABSTRACT FROM AUTHOR] |
