A methodology for machine-learning content analysis to define the key labels in the titles of online customer reviews with the rating evaluation

Abstract

Online reputation is of great strategic importance to companies today. Customers share their emotions and experiences about the service received or the product acquired through online opinions in the form of quantitative variables or text comments. Although quantitative variables can be analyzed using different statistical methods, the main limitation of comment content analysis lies in the statistical analysis because the texts are qualitative. This study proposes and applies a methodology to develop a machine learning designed to identify the key labels related to the quantitative variables in the general rating of the service received from an airline. To this end, we create a quantitative dichotomous variable from zero to one from a database of comment title labels, thus facilitating the conversion of titles into quantitative variables. On this basis, we carry out a multiple regression analysis where the dependent variable is the overall rating and the independent variables are the labels. The results obtained are satisfactory, and the significant labels are determined, as well as their signs and coefficients with the general ratings. Findings show that the significant labels detected in titles positively influence the prediction of the overall rating of airline. This paper is a new approach to applying cluster analysis to the text content of customers’ online reviews in an airline. Thus, the proposed methodology results in a quantitative value for the labels that determines the direction and intensity of customers’ opinions. Moreover, it has important practical implications for managers to identify the weakness and the strengths of their services in order to increase their positioning in the market by developing meaningful strategies.