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INVESTIGATION OF CUSTOMER PREFERENCE CHANGES FOLLOWING COVID-19 MARKET DISRUPTION USING ONLINE REVIEW ANALYSIS

Published online by Cambridge University Press:  19 June 2023

Seyoung Park ,
Kangcheng Lin ,
Junegak Joung  and
Harrison Kim
Seyoung Park
Affiliation:
University of Illinois at Urbana-Champaign;
Kangcheng Lin
Affiliation:
University of Illinois at Urbana-Champaign;
Junegak Joung
Affiliation:
Hanyang University
Harrison Kim*
Affiliation:
University of Illinois at Urbana-Champaign;
*
Kim, Harrison, University of Illinois at Urbana-Champaign, United States of America, hmkim@illinois.edu

Abstract

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COVID-19 pandemic has continued to pose a challenge to the society for almost three years, adversely affecting all segments of population in a scale unseen in the recent decades. Over the course of COVID-19 pandemic, many people have lost their jobs and income. These social and economic impacts have disrupted the market, potentially altering people's attitudes towards different product features. Therefore, this paper investigates the changes in customer preferences on various features of different products, before and after COVID-19 pandemic, using online review analysis. The proposed framework consists of four stages. Firstly, product review data is collected and preprocessed. Secondly, customer interest in product features is explored using latent Dirichlet allocation. Thirdly, customer sentiment for these features is analyzed with Valence Aware Dictionary and sEntiment Reasoner. Finally, the importance of each feature is calculated based on interpretable machine learning. The proposed method is tested on two real-world datasets – smartphone and laptop reviews. The result reveals the changes in customer sentiments and preferences for product features, thus helping companies quickly establish strategies in rapidly changing market environments.

Keywords

Market implicationsUser centred designSemantic data processingMachine learning
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2375 - 2384
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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