Online Social Networks in Business Frameworks

26Improved Supervised Classification Model for Automatically Categorizes of News Articles

Nikhil Chaturvedi^* and Jigyasu Dubey

Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

Abstract

A plethora of online news exists, but its organization is often lacking. Past research has primarily focused on categorizing news, especially in the realm of detecting misinformation. Employing machine learning methods for classifying internet news articles, researchers have leaned towards these approaches due to their computational efficiency and simplicity. This study proposes an enhanced supervised classification strategy for assigning news articles to their respective categories. The suggested approach begins by recognizing event sentences using triggers and sentence arguments. Following that, a machine learning model for sequence labeling will be developed to extract named entities from these event sentences. Furthermore, endeavors will be undertaken to identify contextually similar event sentences. The objective is to establish a sophisticated supervised classification method capable of automatically categorizing articles by identifying events and their associated types.

Keywords: Events, NER, sequence labeling, sentence similarity

26.1 Introduction

As digital media [1, 2] and the internet have become more widely used, the quantity of online news articles has increased dramatically. Unfortunately, the organization of these articles often lacks coherence, posing a challenge for ...