Modeling customer satisfaction through online reviews: A FlowSort group decision model under probabilistic linguistic settings

Abstract

Modeling customer satisfaction from online customer reviews (OCRs) has become a practical issue. OCRs provide enough information to aid service providers in measuring customer satisfaction. However, the voluminous nature and the ambiguity surrounding OCRs increase the difficulty for service providers to measure customer satisfaction. Since different customers have diverse perceptions, priorities, and preferences, it is appropriate to measure customer satisfaction from customer segmentation. Thus, we establish a probabilistic linguistic group decision (PLGD)-FlowSort methodology to model customer satisfaction. This methodology employs the Latent Dirichlet Allocation (LDA) topic model to extract the relevant customer satisfaction dimensions (CSDs) from OCRs. Then, based on the SOM clustering technique, we segment customers based on their satisfaction degree. To analyze OCRs, we compute the sentiment scores of the reviews using an unsupervised machine learning algorithm and convert the sentiment scores into probabilistic linguistic term sets (PLTSs). To objectively determine the weight information of the segments and the CSDs, we design the probabilistic linguistic (PL)-projection method and the probabilistic linguistic-correlation coefficient standard deviation (PL-CCSD) method, respectively. The PLGD-FlowSort is developed to measure customer satisfaction towards several service providers and categorize them into different satisfaction levels. Finally, we apply the proposed methodology to a case of measuring customer satisfaction towards mobile payments in Ghana and further perform a comparative analysis to test the robustness of our work.