摘要
Web 2.0 technologies have attracted an increasing number of people with various backgrounds to become active online writers and viewers. As a result, exploring reviewers＇ opinions from a huge number of online reviews has become more important and simultaneously more difficult than ever before. In this paper, we first present a methodological framework to study the ＂purchasing-reviewing＂ behavior dynamics of online customers. Then, we propose a review-to-aspect mapping method to explore reviewers＇ opinions from the massive and sparse online reviews. The analytical and experimental results with real data demonstrate that online customers can be sectioned into groups in accordance with their reviewing behaviors and that people within the same group may have similar reviewing motivations and concerns for an online shopping experience.
Web 2.0 technologies have attracted an increasing number of people with various backgrounds to become active online writers and viewers. As a result, exploring reviewers＇ opinions from a huge number of online reviews has become more important and simultaneously more difficult than ever before. In this paper, we first present a methodological framework to study the ＂purchasing-reviewing＂ behavior dynamics of online customers. Then, we propose a review-to-aspect mapping method to explore reviewers＇ opinions from the massive and sparse online reviews. The analytical and experimental results with real data demonstrate that online customers can be sectioned into groups in accordance with their reviewing behaviors and that people within the same group may have similar reviewing motivations and concerns for an online shopping experience.
