Spatial Sentiment and Perception Analysis of BBC News Articles Using Twitter Posts Mining

Abstract

Over the past few decades, an exponential growth is seen in social media, online resources and microblogging websites such as Twitter. There has been a gush of user generated content and production of huge amount of data through news and event sharing on these sites is no exception. Data generated by these resources is a rich source of information for data mining. Sentiment Analysis is a current and important research area that attempts to determine the polarity of text. Determining the sentiments on happening events around the globe has become extremely important. In this paper, a subjective lexicon-based approach is proposed to mine the unstructured data into meaningful information from a popular microblogging website, Twitter, in order to determine the semantic orientation of real-time reactions and opinions. The main focus is to extract the audience’s sentiment related to BBC news articles being shared on Twitter. Firstly, our approach will extract all comments on shared articles and determine their polarity. Secondly, it categorizes the extracted users based on their location and shows the collective opinion of users in different regions. Thirdly, a visualization tool has been developed for viewing the obtained results.