Using ML Algorithm to Study Chinese Tourist Behaviour in Malaysia

Tourism is a major industry in Malaysia, but the COVID-19 pandemic has caused a substantial decline in tourism revenue. As the third-largest contributor to Malaysia's tourism income, Chinese tourists play a crucial role in the sector. Therefore, understanding the behavioral preferences and influencing factors of Chinese tourists can aid in the recovery of Malaysia's tourism industry and enhance tourist satisfaction. This study collects user data from two prominent platforms, Xiaohongshu and Fliggy, and applies two analytical algorithms-term frequency-inverse document frequency (TF-IDF) and latent Dirichlet allocation (LDA). These methods enable the extraction of keywords, topics, and sentiment from the text data. The insights gained can help the Ministry of Tourism Malaysia better understand tourist needs, optimize services and products, and make informed strategic decisions to improve competitiveness and business performance.