Empowering text classification with NLP and explainable AI for enhanced interpretability

Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address these challenges and to enhance explainability, this study proposes a novel approach to text classification leveraging natural language processing (NLP) techniques and explainable AI (XAI) methods. Text preprocessing steps were essential for improving the quality of text analysis. This was gained by eliminating elements that contribute minimal semantic value. To achieve robust performance and mitigate the risk of overfitting, repeated stratified K-Fold cross-validation was utilized. Furthermore, the synthetic minority oversampling technique (SMOTE) was employed to address dataset imbalance issues. In the classification phase, nine machine learning models and hybrid/multi-model approaches were employed. To validate the explainability of the classifications, the local interpretable model-agnostic explanations (LIME) framework was utilized. The study utilized two datasets containing texts from domains such as sports, medicine, entertainment, politics, technology, and business. Empirical evaluations demonstrated the effectiveness of the proposed approach. The proposed hybrid model achieved exceptional performance across key metrics, including accuracy, precision, recall, and F1-score. The proposed hybrid model achieved results of up to 99% accuracy. This work can be used for various text analysis applications.