Identifying competitors through comparative relation mining of online reviews in the restaurant industry. (April 2018)

• Record Type: Journal Article
• Authors: Gao, Song; Tang, Ou; Wang, Hongwei; Yin, Pei
• Is Part Of: International journal of hospitality management. Volume 71(2018)
• Page Start: 19
• Page End: 32
• Publication Date: 2018-04
• Subjects: Competitor identification -- Service improvement strategy -- Competitive analysis -- Aspects-comparison relation mining -- Online review -- Restaurant industry; Hotel management -- Periodicals; Restaurant management -- Periodicals; Food service management -- Periodicals; Hôtels -- Gestion -- Périodiques; Restaurants -- Gestion -- Périodiques; Services alimentaires -- Gestion -- Périodiques; Food service management; Hotel management; Restaurant management; Periodicals; 647.94
• DOI: 10.1016/j.ijhm.2017.09.004
• Languages: English
• ISSNs: 0278-4319

Abstract

Highlights: We propose a novel model to extract aspect-oriented comparative relations from publicly available and opinion-rich textual resources. We construct three types of comparison relation networks to better perform the tasks of competitiveness analysis in the restaurant industry. The model provides restaurant managers a solution to enhance their competitiveness, such as competitor identification and market analysis. Our model can be applicable in other industries, thus facilitating managers to perceive and response the variation of market changes. Abstract: It is of importance for restaurants to identify their competitors to gain competitiveness. Meanwhile, opinion-rich resources like online reviews sites can be used to understand others opinion toward restaurant services. We thus propose a novel model to extract comparative relations from online reviews, and then construct three types of comparison relation networks, enabling competitiveness analysis for three tasks. The first network help restaurants analyze market structure for their positioning. The second network enables to identify top competitors using competitive index and dissimilarity index . The third network help restaurants identify strengths and weaknesses through aspects-comparison relation mining. Finally, the market environment is illustrated in a visual way according to the three types of networks. Experimental results reveal the effectiveness of the proposed competitiveness analysis using text analytics, which can identify top competitors and evaluate the market environment, as well as help the focal restaurant effectively develop a service improvement strategy.