Opinion Mining in the Airline Industry: Learning Social Sentiments and Insights

Abstract

Due to growing social network traffic through Internet service provider, businesses are creating applications or systems that tap on social data to study user behavior and in turn, enhance the user experience. This paper proposes an airline opinion mining system (AOMS) that mines and analyzes data from social media such as Twitter, Instagram and Facebook. AOMS is developed using Opinion Mining methodology with four modules, which are Problem Areas, Team Performance, High Priority, and Online Feedback. AOMS was evaluated via Unit Testing to resolve all logical and system errors. The prototype of AOMS system is to be further tested via User Acceptance Test (UAT) by studying user experience in a pilot airline industry.