Explainable detection: a transformer-based language modeling approach for Bengali news title classification with comparative explainability analysis using ML and DL

Classifying scattered Bengali text is the primary focus of this study, with an emphasis on explainability in Natural Language Processing (NLP) for low-resource languages. We employed supervised Machine Learning (ML) models as a baseline and compared their performance with Long Short-Term Memory (LSTM) networks from the deep learning domain. Subsequently, we implemented transformer models designed for sequential learning. To prepare the dataset, we collected recent Bengali news articles online and performed extensive feature engineering. Given the inherent noise in Bengali datasets, significant preprocessing was required. Among the models tested, XLM-RoBERTa Base achieved the highest accuracy 0.91. Furthermore, we integrated explainable AI techniques to interpret the model's predictions, enhancing transparency and fostering trust in the classification outcomes. Additionally, we employed LIME (Local Interpretable Model-agnostic Explanations) to identify key features and the most weighted words responsible for classifying news titles, which validated the accuracy of Bengali news classification results. This study underscores the potential of deep learning models in advancing text classification for the Bengali language and emphasizes the critical role of explainability in AI-driven solutions.