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Research in Natural Language Processing (NLP) has increasingly becomeimportant due to applications such as text classification, text mining,sentiment analysis, POS tagging, named entity recognition, textual entailment,and many others. This paper introduces several machine and deep learningmethods with manual and automatic labelling for news classification in theBangla language. We implemented several machine (ML) and deep learning (DL)algorithms. The ML algorithms are Logistic Regression (LR), Stochastic GradientDescent (SGD), Support Vector Machine (SVM), Random Forest (RF), and K-NearestNeighbour (KNN), used with Bag of Words (BoW), Term Frequency-Inverse DocumentFrequency (TF-IDF), and Doc2Vec embedding models. The DL algorithms are LongShort-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit(GRU), and Convolutional Neural Network (CNN), used with Word2vec, Glove, andFastText word embedding models. We develop automatic labelling methods usingLatent Dirichlet Allocation (LDA) and investigate the performance ofsingle-label and multi-label article classification methods. To investigateperformance, we developed from scratch Potrika, the largest and the mostextensive dataset for news classification in the Bangla language, comprising185.51 million words and 12.57 million sentences contained in 664,880 newsarticles in eight distinct categories, curated from six popular online newsportals in Bangladesh for the period 2014-2020. GRU and Fasttext with 91.83%achieve the highest accuracy for manually-labelled data. For the automaticlabelling case, KNN and Doc2Vec at 57.72% and 75% achieve the highest accuracyfor single-label and multi-label data, respectively. The methods developed inthis paper are expected to advance research in Bangla and other languages.

Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language