Year: 2025
Type: conference-paper
Abstract: A substantial amount of data is generated day-to-day, to which news articles are a major contributor. Most of this data is not well-structured, highlighting the need for efficient ways to manage, process, and analyze said data. One useful approach involves the categorization of the data. The work “News Article Category Classification” develops a Long Short-Term Memory (LSTM) model for classifying news articles into 14 categories. LSTM networks are suitable for text classification tasks, as they efficiently capture contextual and sequential dependencies. They have a special ability to retain long-term information which makes them perfect for understanding the meaning of news articles.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7117299493