Supervised Machine Learning for Multi-label Classification of Bangla Articles

Abstract

Multi-label text classification has been a key point of research in the area of text classification latterly. But to the best of our knowledge, there have been very few research on multi-label text classification for Bangla text. There is also inadequacy of proper dataset for multi-label classification on Bangla text. Multi-label classification has many applications in the real world. One of them is automated labeling of articles of online news portals so that readers can easily look up other news articles on similar topics by clicking on hyperlinks. We applied supervised multi-label classification techniques on Bangla news articles for automated tag generation to predict related topics. We have built a new dataset from scratch and applied various problem transformation methods for multi-label classification with naive bayes classifier, logistic regression and SVM. We have analyzed the performance of these algorithms on Bangla news articles with precision, recall, f1-score and hamming loss. The dataset and the analysis of the results can be valuable for further research on multi-label text classification of Bangla text. We have open-sourced the dataset and the source code of this work (http://bit.ly/34cSNCR).