News Sentiment Analysis

TL;DR

This paper presents a lexicon-based method for analyzing emotions in news articles, validated on BBC news data, to automate understanding of human sentiments in large-scale textual news data.

Contribution

It introduces a lexicon-based approach specifically for news sentiment analysis and demonstrates its effectiveness on a real-world BBC news dataset.

Findings

Effective sentiment classification on BBC news data

Automates emotion detection in news articles

Validates approach with experimental results

Abstract

Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology. IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job, thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events that comprise of emotions - good, bad, neutral. Sentiment analysis is utilized to investigate human emotions present in textual information. This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news data set, which expresses the applicability and validation of the adopted approach.