Identifying consumer preferences from user-generated content on amazon.com by leveraging machine learning

Abstract

Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumers' prior reviews can be useful for inexperienced consumers. However, one-sided review systems (e.g., Amazon) only provide the opportunity for consumers to write a review as a buyer and contain no feedback from the seller's side, so the information displayed about individual buyers is limited. Therefore, this study analyzes consumers' digital footprints (DFs) for programmable thermostats to identify and predict unobserved consumer preferences, using a dataset of 141 million Amazon reviews. This paper proposes novel approaches (1) to identify unobserved consumer characteristics and preferences by analyzing the target consumers' and other prior reviewers' DFs; (2) to extract product-specific product content dimensions (PCDs) from review text data; (3) to predict individual consumers' sentiment before they make a purchase or write a review; (4) to classify consumers' sentiment toward a specific PCD by using context-based word embedding and deep learning models. Overall, this approach developed in this paper is applicable, scalable, and interpretable for distinguishing important drivers of consumer reviews for different goods in a specific industry and can be used by industry to design customer-oriented marketing strategies.