Online shopping behavior study based on multi-granularity opinion mining: China vs. America
Qingqing Zhou, Rui Xia, Chengzhi Zhang

TL;DR
This paper introduces an automated, multilingual opinion mining approach to study and compare customer behavior in China and America using online reviews, providing insights into cultural differences in product preferences.
Contribution
It presents a novel automated system for multilingual opinion mining and questionnaire generation, enabling large-scale, cost-effective international customer behavior analysis.
Findings
Chinese customers use euphemistic expressions and follow the Doctrine of the Mean.
American customers express opinions more directly.
Chinese customers focus on external features, Americans on internal features.
Abstract
With the development of e-commerce, many products are now being sold worldwide, and manufacturers are eager to obtain a better understanding of customer behavior in various regions. To achieve this goal, most previous efforts have focused mainly on questionnaires, which are time-consuming and costly. The tremendous volume of product reviews on e-commerce websites has seen a new trend emerge, whereby manufacturers attempt to understand user preferences by analyzing online reviews. Following this trend, this paper addresses the problem of studying customer behavior by exploiting recently developed opinion mining techniques. This work is novel for three reasons. First, questionnaire-based investigation is automatically enabled by employing algorithms for template-based question generation and opinion mining-based answer extraction. Using this system, manufacturers are able to obtain…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Digital Marketing and Social Media
