A Sequential Learning on Bengali News Title through Semantic Learning with Explainable AI

Abstract Classifying scattered Bengali text is the primary purpose of this study. Our study classified Bengali news titles with Machine Learning (ML) models, particularly focusing on LSTM networks. The goal was to categorize Bengali news into labeled categories and contributing to the growing field of NLP for low-resource languages. For classification, some supervised ML models are used and LSTM for sequential learning. However, LSTM scores the highest accuracy of 81.70%. Additionally, we incorporated explainable AI techniques to decipher the model’s predictions, ensuring transparency and trustworthiness in the classification process. Our study demonstrates the potentiality of deep learning models in improving text classification tasks for the Bengali language and highlights the importance of explainability in AI-driven solutions.