텍스트마이닝을 활용한 호텔 서비스 로봇 시장동향 분석

Analysis of the Hotel Service Robot Market Trend Using Text Mining

Purpose – In order to identify the market trend of hotel service robots, this study aims to conduct a big data analysis using text mining by dividing it into pre-COVID-19, COVID-19, and post-COVID-19 periods. In other words, we will identify the issues in the hotel service robot market trend and make meaningful suggestions through network analysis and the centrality results of key texts, key correlations by period, and a comparison of co-occurring keywords. Design/Methodology/Approach – Frequency analysis and sentiment analysis were performed using texts from the major portal sites Daum, Naver, and Google, and QAP correlation analysis was performed to confirm the structure of the network for each period, including before COVID-19, during COVID-19, and after COVID-19 related to service robots. Lastly, co-occurrence keyword analysis was performed to identify interrelated keywords. Findings – The results of the analysis are as follows. As a result of the frequency analysis, before COVID-19, it was presented in the order of service, Seoul, and artificial intelligence; during COVID-19, service, Seoul, and introduction; and after COVID-19, service, market, and business. In addition, the results of the power centrality analysis show a similar structure to the betweenness centrality results. Third, as a result of QAP correlation analysis, it was confirmed to be statistically significant, from a minimum of .838 to a maximum of .951. Research Implications – Based on the analysis results, hotel managers will need solutions for food service as well as investments and improvements in service and robots, and will need to find ways to provide more value to customers.