A multigene genetic programming-based fuzzy regression approach for modelling customer satisfaction based on online reviews

Abstract

In previous studies, customer survey data were commonly adopted to perform the modelling of customer satisfaction (CS). However, it could be time-consuming to conduct surveys and obtain their data. On the other hand, respondents' responses are quite often confined by pre-set questions. Nowadays, a huge number of customer online reviews on products can be found on various websites. The reviews can be extracted easily in a very short time. Customers can freely express their concerns and views of products in their online reviews. Those reviews provide a valuable source of information for manufacturers to improve their existing products and develop their new products. Previous studies have attempted to develop CS models based on survey data by using various computational intelligence techniques. However, no attempt at developing explicit CS models based on online reviews was reported in literature. In this paper, a methodology for the modelling of CS based on customer online reviews and a multigene genetic programming based fuzzy regression (MGGP-FR) approach is proposed. In the proposed methodology, relevant textual reviews of products are extracted from e-commerce websites. Then, opinion mining is conducted on the reviews and sentiments scores of customer concerns are derived. A MGGP-FR approach is then introduced to develop CS models based on the derived sentiment scores. A case study on developing CS models for electronic hairdryers is conducted to illustrate the proposed methodology and validate the effectiveness of MGGP-FR in the modelling of CS. The validation results show MGGP-FR outperforms the other three modelling approaches, fuzzy regression, genetic programming, and genetic programming based fuzzy regression, in the CS modelling in terms of prediction accuracy.

Developing explicit customer preference models using fuzzy regression with nonlinear structure

“…A dynamic evolving neural-fuzzy inference system was applied for the modeling of variational customer preferences for the design of hair dryers [ 35 ]. Yakubu et al [ 36 ] proposed a multigene genetic programming-based fuzzy regression approach to develop customer preference models based on online reviews. But in addition to the need to address the problem of vagueness, scholars have found that when modeling product design attributes and consumer preferences, the relationships are often highly nonlinear.…”

Analysis of airbnb’s green user emotional characteristics: How do human, geographical, housing, and environmental factors influence green consumption?

“…Lawani et al (2018) used the sentiment dictionary to obtain the sentiment scores of reviews as a measure of user satisfaction, and explored the impact of price and distance on user satisfaction. Yakubu et al (2021) used multi-gene genetic programmingbased fuzzy regression (MGGPFR) to obtain the sentiment scores of reviews to analyze the sentiment tendencies of e-commerce users. It is worth noting that the emotional tendencies of a single user can be evaluated by sentiment values.…”

“…When analyzing the emotional characteristics of users, researchers often find the emotional tendencies of users by calculating the sentiment value of reviews (Lawani et al, 2018;Yakubu et al, 2021). Some studies use supervised machine learning models such as deep learning to train labeled samples, so as to predict users' emotional tendencies (Liu et al, 2021).…”

Neuro-fuzzy network incorporating multiple lexicons for social sentiment analysis

“…The second step is to classify tweets using multilayer perceptron model. In a recent research work (Yakubu et al 2021 ), customer satisfaction model based on online reviews has been developed, based on the sentiment scores derived by applying a novel multigene genetic programming-based fuzzy regression (MGGP-FR) approach. To address the fuzziness of customer opinions, the sentiment scores are transformed into asymmetrical fuzzy numbers.…”