The Mobility, Sentiment and Problems Identification Analysis in Tourism Industry using Social Media Data

Abstract

Tourism industries have the potential to contribute to the country's income, and as they should, we expect this industry to continue to grow each year. Indonesia is one of the well-known countries with incredible destinations to visit by domestic and international tourists that are continuously growing. There are many ways to determine a suitable strategy to understand tourist behavior, such as tourist mobility, sentiment, and problem analysis. Using tourist reviews or user-generated content (UGC) data on the Tripadvisor website, we employ social network analysis (SNA) to identify tourist mobility, favorites, and in-between destination using network metrics and measurements. We use sentiment analysis to classify tourist sentiment and the multiclass text classification method to find various problems in tourist reviews. We also construct a text corpus for the tourism domain to classify tourism problems. The results represent the complex tourist mobility to recognize the favorite destination and the movement visualization to determine the crowded area, the tourist sentiment, and the tourist problem in Bali tourism destination. The combined model benefits many stakeholders such as tourists, the government, and business organizations.