The Effect of Online Reviews on Ebook Pricing: A Text Analytics Approach In this paper, we explore how online reviews affect ebook prices via analyzing the sheer volume online data from the ebook websites. Namely, we first employ a domain ontologybased method to select the most discriminative features that may affect the ebook prices. Then, the topic modeling method latent Dirichlet allocation and aspectoriented sentiment analysis method are applied as a supplement. Using the multiple regression method, we identify the key attributes that may influence the prices of ebooks and give the related regression equation. The managerial implication is that firms can obtain a reference price for an ebook and may dynamically adjust the price to increase ebook sales according to our data analysis results.

Discovering Relevant Reviews for Answering ProductRelated Queries With the increasing popularity of ecommerce, the number of productrelated queries generated by customers is growing. Answering these queries manually in real time is infeasible, and so automatic questionanswering systems can be immensely helpful. Product queries are, however, very different from opendomain questions: they tend to be productspecific and the answers they demand can be very subjective. Previous research suggests that reviews are a valuable resource for answering product queries, but a key challenge is the language mismatch between user queries and reviews. To address this, we propose two neural models that discover relevant reviews for answering product queries. We demonstrate that our best model produces strong performance, outperforming stateoftheart systems by consistently finding the most relevant reviews for product queries.