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Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumers&#x2019; 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&#x2019;s side, so the information displayed about individual buyers is limited. Therefore, this study analyzes consumers&#x2019; 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&#x2019; and other prior reviewers&#x2019; DFs; (2) to extract product-specific product content dimensions (PCDs) from review text data; (3) to predict individual consumers&#x2019; sentiment before they make a purchase or write a review; (4) to classify consumers&#x2019; 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.

Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning