Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes
Knowledge is central to human and scientific developments. Natural Language
Processing (NLP) allows automated analysis and creation of knowledge. Data is a
crucial NLP and machine learning ingredient. The scarcity of open datasets is a
well-known problem in machine and deep learning research. This is very much the
case for textual NLP datasets in English and other major world languages. For
the Bangla language, the situation is even more challenging and the number of
large datasets for NLP research is practically nil. We hereby present Potrika,
a large single-label Bangla news article textual dataset curated for NLP
research from six popular online news portals in Bangladesh (Jugantor,
Jaijaidin, Ittefaq, Kaler Kontho, Inqilab, and Somoyer Alo) for the period
2014-2020. The articles are classified into eight distinct categories
(National, Sports, International, Entertainment, Economy, Education, Politics,
and Science \& Technology) providing five attributes (News Article, Category,
Headline, Publication Date, and Newspaper Source). The raw dataset contains
185.51 million words and 12.57 million sentences contained in 664,880 news
articles. Moreover, using NLP augmentation techniques, we create from the raw
(unbalanced) dataset another (balanced) dataset comprising 320,000 news
articles with 40,000 articles in each of the eight news categories. Potrika
contains both the datasets (raw and balanced) to suit a wide range of NLP
research. By far, to the best of our knowledge, Potrika is the largest and the
most extensive dataset for news classification.