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Segmenting and Characterizing Adopters of E-Books and Paper Books Based on Amazon Book Reviews

  • Lu Guan
  • Yafei Zhang
  • Jonathan ZhuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 669)

Abstract

Online product reviews through which consumers express their opinions and experiences with products are extremely valuable for both potential buyers to make informed purchase decisions and retailers to improve their products/services and adjust existing marketing strategies. One of the key challenges for mining product reviews is how to obtain a “ground truth” to guide the segmentation of reviewers properly. We propose a behavior-to-opinion approach, in which users are first categorized based on some unambiguous behavioral patterns (if available) and their online reviews are then classified to reveal unique and detailed characteristics of each user category. In this paper, we identify four categories of book consumers (i.e., kindle-only, print-only, print-to-kindle, and kindle-to-print) based on the long-term patterns of their review behavior. Their review posts are then clustered through word2vec and K-means, and four categories of adopters are matched with their concerned word topics. Finally, we find that print-only adopters show significantly different patterns on content-oriented topics as compared to other three groups. Kindle-to-print adopters pay more attention on portability whereas print-to-kindle adopters stress more on money and user experience. Taken together, our work indicates a diversity of characteristics among four categories of book reviewers.

Keywords

Text analytics Behavioral patterns E-books Product reviews 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Web Mining Lab, Department of Media and CommunicationCity University of Hong KongKowloonHong Kong SAR, China
  2. 2.Key Laboratory of System Control and Information Processing, Ministry of Education of China, Department of AutomationShanghai Jiao Tong UniversityShanghaiChina

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