Attribute Embedding: Learning Hierarchical Representations of Product Attributes from Consumer Reviews

Abstract

Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.