MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification

Alina Petukhova () and Nuno Fachada
Additional contact information
Alina Petukhova: COPELABS, Lusófona University, Campo Grande 376, 1749-024 Lisbon, Portugal
Nuno Fachada: COPELABS, Lusófona University, Campo Grande 376, 1749-024 Lisbon, Portugal

Data, 2023, vol. 8, issue 5, 1-7

Abstract: This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.

Keywords: news dataset; text classification; NLP; media topic taxonomy (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/8/5/74/pdf (application/pdf)
https://www.mdpi.com/2306-5729/8/5/74/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:8:y:2023:i:5:p:74-:d:1130936

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().