Exploring Sentiment Classification Techniques in News Articles

The emergence of web 2.0 applications has greatly contributed to the increase in volume of information available online today. User generated content can help organizations realize the demands of the public be it in e-commerce, politics or newsrooms. Sentiment analysis plays a pivotal role in the mining of such information thus it is a crucial tool not only in organizations’ decision making process but also to the general users of a particular service. Most research on sentiment analysis focuses on subjective text like in micro-blogging, product and movie reviews. News articles sentiment analysis can be a bit difficult considering the need by journalists to remain neutral. Polarity of sentiments is not explicit therefore classification of people’s sentiments in such a scenario is crucial. In this research we will outline the various methodologies used for polarity detection and analysis in news articles. Keywords: Web 2.0 Applications, User Generated Content, Sentiment Analysis, Subjective Text, Classification, Polarity Detection