Temporal analysis of comparative opinion mining

Abstract

Social media have become a popular platform for people to share their opinions and emotions. Analyzing opinions that are posted on the web is very important since they influence future decisions of organizations and people. Comparative opinion mining is a subfield of opinion mining that deals with identifying and extracting information that is expressed in a comparative form. Due to the fact that there is a huge amount of opinions posted online everyday, analyzing comparative opinions from a temporal perspective is an important application that needs to be explored. This study introduces the idea of integrating temporal elements in comparative opinion mining. Different type of results can be obtained from the temporal analysis, including trend analysis, competitive analysis as well as burst detection. In our study we show that temporal analysis of comparative opinion mining provides more current and relevant information to users compared to standard opinion mining.