In the mobile game industry, modelling customer requirements (CRs) is becoming indispensable for developers before improving games. Online reviews, as a common way of customer feedback, can reflect customers' real experiences and preferences. Several studies have extracted important CRs from online reviews of mobile games, but they have not thoroughly analysed the correlation with customer satisfaction. In this study, we propose a CR modelling framework to extract and classify CRs from online reviews and analyse the influence of each CR on customer satisfaction to assist developers in improving games. First, we extract important CRs based on word embedding from online reviews. Then, we construct a BERT-Word2Vec-Convolutional Neural Network (BW-CNN) model to analyse the sentiment of online reviews by considering semantic features at Chinese character and word granularity. Finally, based on the Shapley Additive Explanation (SHAP) method, the S-Kano model is further proposed to classify the CRs considering customer attention, and the product improvement suggestions are provided for the corresponding mobile game developers based on the CR classification results. To verify the effectiveness and advantages of the proposed method, we crawled the online reviews of four Gacha games from taptap (www.taptap.cn) for case study and compared with the existing methods. The results show that our proposed framework is effective in modelling CRs from online reviews of mobile games. Meanwhile, the results comparison illustrate that customer attention has a significant influence on the CRs classification. In addition, we find that the S-Kano categories of CRs vary across games, and there is strong heterogeneity among games.
