Categorization of News Articles using Sentiment Analysis

Abstract

The advent use of new online social media such as articles, blogs, message boards, news channels, and in general web content has dramatically changed the way people look at various things around them. Today, it’s a daily practice for many people to read news online. People's perspective tends to undergo a change as per the news content they read. The majority of the content that we read today is on the negative aspects of various things e .g. corruption, rapes, thefts etc. Reading such news is spreading negativity amongst the people. Positive news seems to have gone into a hiding. The positivity surrounding the good news has been drastically reduced by the number of bad news. This has made a great practical use of Sentiment Analysis and there has been more innovation in this area in recent era. It traditionally emphasizes on classification of text document into positive and negative categories. The objective of this paper is to provide a platform for serving good news and create a positive environment. This is achieved by finding the sentiments of the news articles and filtering out the negative articles which carry negative sentiments. This would enable us to focus only on the good news which will help spread positivity around society and would allow people to think positively. To achieve our objective, we have proposed an algorithm for classification of News articles. This includes data aggregator tool and processing engine at the server side as a Sentiment classifier and a platform for user where positive news being served to read.