Modeling customer satisfaction from unstructured data using a Bayesian approach

Abstract

The Internet is host to many sites that collect vast amounts of opinions about products and services. These opinions are expressed in written language, and this paper presents a method for modeling the aspects of overall customer satisfaction from free-form written opinions. Written opinions constitute unstructured input data, which are first transformed into semi-structured data using an existing method for aspect-level sentiment analysis. Next, the overall customer satisfaction is modeled using a Bayesian approach based on the individual aspect rating of each review. This probabilistic method enables the discovery of the relative importance of each aspect for every unique product or service. Empirical experiments on a data set of online reviews of California State Parks, obtained from TripAdvisor, show the effectiveness of the proposed framework as applied to the aspect-level sentiment analysis and modeling of customer satisfaction. The accuracy in terms of finding the significant aspects is 88.3%. The average R2 values for predicted overall customer satisfaction using the model range from 0.892 to 0.999.