Abstract
Customer segmentation divides customers into groups with different characteristics, and supports the design of customized products and tailored marketing strategies. Recent studies explore using online reviews as the data source and social network analysis as the fundamental technique for customer segmentation. However, few of them investigate the influence of different types of information (e.g., sentiment, order information) from online reviews on the segmentation performance and tackle the challenge of clustering high-dimensional data when online reviews contain customers' rich opinions. To fill this gap, we propose a comprehensive framework for customer segmentation and need analysis based on sentiment network of online reviewers and graph embedding. The frequently mentioned product attributes and customers' sentiments are first extracted from online reviews. Then a customer can be represented as a vector consisting of his/her sentiment polarities on each product attribute. After that, a social network of customers is established by examining the similarity of customer vectors. The network nodes are embedded into low-dimensional vectors, which can be further clustered into different customer segments. A case study employing the online reviews of a passenger vehicle in China's market is used to demonstrate the validity of the proposed framework. The results indicate that the customer segmentation generated by the sentiment network of online reviewers with Graph Autoencoder (GAE) embeddings performs better than other alternative models. Our framework provides more nuanced insights for designers to improve customers' satisfaction and increase the market competitiveness of their products.
