Analyzing Tourism Mobile Applications Perceived Quality using Sentiment Analysis and Topic Modeling

Abstract

Mobile application is one of the most important information platforms for international tourists. Millions of tourists use mobile applications to find information and make transactions. Two popular Online Travel Agent (OTA) mobile applications for travel-related activities providers are Traveloka and Tiket.com. These applications certainly must meet travelers’ needs to achieve satisfaction. Such satisfaction related to application Mobile Application Service Quality (MappSql) dimensions can be traced from thousands of their comments on the Google Play Store. From a set of reviews, information about the perception of mobile application quality can be obtained. Knowledge on user perceptions is very useful for company’s consideration in creating effective business and app features to increase users’ satisfaction. We propose Text Mining models to bring up hidden information regarding users’ verdict. The selected text analysis methods for this research are Sentiment Analysis and Topic Modeling. We find that positive or negative sentiments towards MappSql dimensions of online travel agent applications qualities can be revealed using sentiment analysis method. Topic Modeling method is used to bring up groups of important words of topics related to each mobile application service quality dimensions.