Determination of news biasedness using content sentiment analysis algorithm

Abstract

Nowadays, identifying news biases in the social media is one of the most fundamental problems. News bias is a complex process that comprises several dimensions to be taken into account and it is interlinked with social, political and economic problems.  In general, news bias has the ability to reflect opinion of people about a topic or government policies and actions.  The proposed algorithm develops a system which can detect the biasedness of news topics from different news Websites.This approach automatically collects the news contents from various online news media portals and then consolidates them for the determination of news biasedness. In the experimental study, the news topics are gathered from various Websites of U.S., U.K., and India. For training dataset 3265 news sentences were collected under various news topics from 20 different news Websites. The effectiveness of classification of algorithm is proved by the extensive experimental study. The proposed algorithm provides a method improves the determination of news biasedness, which in turn may help in providing impartial, unbiased and reliable information.