Applications of opinion mining to data journalism

Abstract

Nowadays social media play a central role in every day life. A huge volume of user-generated data spins around online social networks, such as Twitter, having an extraordinary impact on the media industry and on the users’ everyday life. More and more users and people use social networks from their computers and smartphones to share their emotions and opinions about the facts happening in the world. Natural language processing and, in particular, sentiment analysis are key technologies to make sense out of the data about news that circulates in the online social networks. The application of opinion mining to news-oriented user-generated contents, such as news-linking tweets, can provide novel views on the news audience behaviour and help to interpret the evolution of sentiments. Applying this capability in the social news-sphere permits (i) to measure the impact of news onto readers and (ii) to gather elements that contain stories. From a broad perspective, the main aim of this research is to face this challenge, that is, to explore how opinion mining (or sentiment analysis) can be adopted into the field of digital media and data-driven journalism.