Exploring Online Customer Reviews of Luxury Hotels in Malaysia

Abstract

Online customer reviews can provide service companies with a better insight into customer choices. Understanding online customer feedback can help hotel companies develop an effective marketing strategy and differentiate the levels of service and experience they provide to the customers. This can increase the level of hotel customer satisfaction and positively the impact decision-making process. Therefore the purpose of this study is to analyze the content of online customer reviews to reveal the perception of the hotel customers through text mining and sentiment analysis. The study was based on six top 5-star hotels selected from Google Travel and located in Kuala Lumpur, Malaysia. To achieve the aims of the study more than 15,000 distinct data points of the sample were collected from SCTM 3. Moreover, the R programming language was used for deriving word matrix and word count to analyze the data, while UCINET was applied for semantic network analysis of top frequency words. In addition, CONCOR analysis was performed to divide keywords into four clusters: “Service”, “Scenario”, “Environment”, and “Destination” and visualized by drawing networks and nodes using NetDraw in UCINET. The results of the study provide important empirical evidence with implications for building effective marketing strategies for 5-star hotels in Kuala Lumpur.