Text Mining with Network Analysis of Online Reviews and Consumers’ Satisfaction: A Case Study in Busan Wine Bars

Abstract

With the growth of internet technology, customers are sharing up their experiences. Hence, these types of customer experiences are spreading rapidly as a source of online reviews. For this reason, online reviews have become a critical source of information that influences customers’ purchase intentions and behavior. Thus, businesses should monitor online reviews to understand the customer experience and increase customer satisfaction and loyalty. This study attempts to identify essential characteristics for positive online reviews of wine bars and examine the structural relationships of these attributes. To accomplish this purpose, a total of 1,337 online reviews were collected from Google Travel and analyzed. The frequency analysis was performed using text mining to determine the most frequently referred to attributes, and the semantic network analysis, factor analysis, and regression analysis were conducted to understand customer experience and satisfaction of wine bars located in Busan, South Korea. The results show that the top 50 keywords identified from the online reviews were categorized as four groups—‘Atmosphere’, ‘Service’, ‘Date and Location’, and ‘Menu’. The results of the factor analysis reduced the original dimension of 48 keywords to 16 keywords and classified them into six factors, namely, ‘Service’, ‘Staff’, ‘Menu’, ‘Environment’, ‘Recommendation’ and ‘Atmosphere’. Based on these results, implications for sustainable wine bar marketing strategies were suggested.

Keywords

• Busan wine bar
• customer satisfaction
• customer experience
• online review
• semantic network analysis
• eWOM