Detection of controversial issues and their subtopics using sentiment analysis

Abstract

People often want to know about how others think negatively or positively about controversial issues and their subtopic (reason). We define a controversial issue as a concept that invokes conflicting sentiments or views and a subtopic as a reason or factor that gives a particular sentiment or view to the issue. Given the definitions, we propose a method for automatically detecting controversial issues and their subtopics for news articles. For controversial issue detection, we consider the magnitude of sentiment information and the difference between the amounts of two different polarity values. Also, for subtopic identification, we extract candidate phrases and apply a statistical classifier using five different features, some of which attempt to capture the relationship between the identified issue phrase and the candidate subtopic phrase using term distribution information and sentiment clues. Through an experiment and analysis using the MPQA corpus consisting of news articles, we found that the proposed method is promising for both of the tasks although many additional research issues remain to be tapped in the future.