Explainable Machine Learning Models for Swahili News Classification
Sudi Murindanyi, Brian Afedra Yiiki, Andrew Katumba, Joyce Nakatumba Nabende · 2023
Although Swahili is considered a well-resourced language, challenges persist in utilizing it for Natural Language Processing (NLP) tasks, primarily due to the limited data availability required for these systems. For instance, obtaining sufficient Swahili news data for classification remains a significant obstacle. This paper addresses the problem of accurate Swahili news classification by leveraging classical machine learning (ML) models and deep neural networks (DNN). Our proposed method involves data acquisition, Exploratory Data Analysis (EDA), and employing modelling techniques using classical ML models, such as Support Vector Machine (SVM), Logistic Regression, Multinomial Naive Bayes, Random Forest, Gradient Boosting, Hard Voting, and Bagging, as well as DNN models including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and CNN-Bi-LSTM + Attention. The models were evaluated using Area Under the Curve (AUC) metrics and accuracy. Our results demonstrate commendable performance for classical ML classifiers and DNN models, with accuracies above 75%. Notably, the CNN-Bi-LSTM + Attention model achieved an impressive AUC score of 97%. Additionally, explainability using LIME (Local Interpretable Model-agnostic Explanations) provided valuable insights into model decisions. This research contributes to Swahili natural language processing and lays the foundation for further explorations into transformer-based models for improved classification.