Applying text mining and semantic network analysis to investigate effects of perceived crowding in the service sector

Abstract

Semantic analysis is becoming increasingly important not only in computing but also in the business world. The purpose of the current study is to apply semantic network analysis to the service industry, one of the economic sectors. To learn more about the crowded environment in the service sector, the study interviewed customers and employees by using dyad approach in the service sector. The data collected was analyzed using a text mining approach in Python library and Ucinet software. The text data collected through interviews was analyzed using multiple techniques like sentiment analysis, centrality analysis, and CONCOR analysis. The results from the two data sets of interviews with employees and consumers revealed certain effects and behavior that they exhibit in a crowded environment. When providing services to consumers in a crowded environment, employees experience a variety of behavioral changes, whether due to physical, psychological, emotional, habitual, or work-related factors. Additionally, findings show that crowding has an emotional and psychological impact on customers’ behavioral responses. The study offers important implications of text analysis for business intelligence.