Intuitionistic fuzzy and multi-criteria based ranking of mobile payment apps using sentiment score of online reviews

With the emergence of digital markets, mobile payment apps have gained popularity for making online transactions due to a number of factors which include convenience, performance, safety, trust to name a few. Customers seek information about these payment apps from the online reviews and the ratings given by the existing users. This study focuses on research questions (1) To identify the key features of m-payment apps and their importance for the customers; (2) To understand the opinion of the customers about the mobile apps with respect to these key features, and (3) To rank five most frequently used m-payment apps in India. For the identification of the key features, Latent Dirichlet Analysis (LDA) model has been applied on the one-year reviews dataset of five payment apps. To evaluate the opinion of the customers about the apps sentiment analysis have been performed on the review dataset. The ranking of the apps has been done by integrating the intuitionistic fuzzy analytical hierarchical process (IF-AHP) and intuitionistic fuzzy TOPSIS (Theory with Technique for order performance by similarity to ideal solution) method. For empirical support of the proposed modeling, we present and analyze a case study with 3,70,051 online reviews. Further, to illuminate the characteristics and benefits of the proposed methodology, we compare the results of the proposed modeling framework with the IF-VIKOR and IF-MOORA multi-criteria decision making techniques. The comparative results with the proposed method on the case study demonstrate the contribution of the proposed approach for sentiment score based ranking of mobile payment apps.