A Model of Discovering Customer Insights in Tourism Sector Approach to Vietnamese Reviews Analytics

Abstract

Today, customer-centric business models are more focused. In addition, businesses increasingly own more data about customers and user behavior, especially from social networking platforms. However, the effective exploitation and use of this amount of data is still very limited. Based on those objective motivations, this research proposes a model as a foundation for analyzing and understanding customers to help businesses transform and transform this data into forms of knowledge to support the business, make intelligent business decisions. A data set of 90,513 reviews in Vietnamese from Agoda e-commerce websites in the tourism sector is collected and experimented. In experimental phase, raw corpus was cleaned by text preprocessing, then, the research will use the Net Promoter Score (NPS) to separate the data into three data sets according to three groups of Detractors, Passives and Promoters. From that, Latent Dirichlet Allocation (LDA) was built to extract the hidden topics in the input dataset. The experimental results show that the extracted topics and their features from corpus of customer reviews are reliable. The novelty in this research is to identify the topics that customers are interested in and complaining about.