  Research in Natural Language Processing (NLP) has increasingly become
important 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 learning
methods with manual and automatic labelling for news classification in the
Bangla language. We implemented several machine (ML) and deep learning (DL)
algorithms. The ML algorithms are Logistic Regression (LR), Stochastic Gradient
Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest
Neighbour (KNN), used with Bag of Words (BoW), Term Frequency-Inverse Document
Frequency (TF-IDF), and Doc2Vec embedding models. The DL algorithms are Long
Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit
(GRU), and Convolutional Neural Network (CNN), used with Word2vec, Glove, and
FastText word embedding models. We develop automatic labelling methods using
Latent Dirichlet Allocation (LDA) and investigate the performance of
single-label and multi-label article classification methods. To investigate
performance, we developed from scratch Potrika, the largest and the most
extensive dataset for news classification in the Bangla language, comprising
185.51 million words and 12.57 million sentences contained in 664,880 news
articles in eight distinct categories, curated from six popular online news
portals in Bangladesh for the period 2014-2020. GRU and Fasttext with 91.83%
achieve the highest accuracy for manually-labelled data. For the automatic
labelling case, KNN and Doc2Vec at 57.72% and 75% achieve the highest accuracy
for single-label and multi-label data, respectively. The methods developed in
this paper are expected to advance research in Bangla and other languages.
