Predicting customer satisfaction based on online reviews and hybrid ensemble genetic programming algorithms

Determination of the design attribute settings of a new product is essential for maximizing customer satisfaction. A model is necessary to illustrate the relation between the design attributes and dimensions of customer satisfaction such as product performance, affection and quality. The model is commonly developed based on customer survey data collected from questionnaires or interviews which require a long deployment time; hence the developed model cannot completely reflect the current marketplace. In this paper, a framework is proposed based on online reviews in which past and current customer opinions are included to develop the model. The proposed framework overcomes the limitation of the aforementioned approaches in which the developed models are not up-to-date. Indeed, the proposed framework develops models based on machine learning technologies, namely genetic programming, which has better generalization capabilities than classical approaches, and has higher transparency capabilities than implicit modelling approaches. To further enhance the prediction capability, committee member selection is proposed. The proposed selection method improves the currently used selection method which trains several models and only selects the best one. The proposed selection method generates a hybrid model which integrates the predictions of the generated models. Each prediction is weighted by how likely the prediction is agreed by others. The proposed framework is implemented on electric hair dryer design of which online reviews in amazon.com are used. Experimental results show that models with more accurate prediction capabilities can be generated by the proposed framework.