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ANALYSIS OF CUSTOMER SENTIMENT ON PRODUCT FEATURES AFTER THE OUTBREAK OF CORONAVIRUS DISEASE (COVID-19) BASED ON ONLINE REVIEWS

Published online by Cambridge University Press:  27 July 2021

Jinju Kim ,
Seyoung Park  and
Harrison Kim
Jinju Kim
Affiliation:
University of Illinois at Urbana-Champaign
Seyoung Park
Affiliation:
University of Illinois at Urbana-Champaign
Harrison Kim*
Affiliation:
University of Illinois at Urbana-Champaign
*
Kim, Harrison, University of Illinois at Urbana-Champaign, Industrial and Enterprise Systems Engineering, United States of America, hmkim@uiuc.edu

Abstract

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The outbreak of the coronavirus disease not only caused many deaths worldwide but also severely affected the development of the global economy, such as supply chain disruptions, plummeted demand, unemployment, etc. These social changes have led to changes in customers' purchasing patterns. Therefore, it is more important than ever for manufacturers to quickly identify and respond to changing customer purchasing patterns and requirements. However, few studies have been done on dynamic changes in customer preferences for product features following COVID-19 spread. This study aims to investigate the dynamic change of customer sentiment on product features following COVID-19 through sentiment analysis based on online reviews. The proposed methodology consists of two main processes: feature extraction and sentiment analysis. After finding a specific feature of the product through feature extraction, the words used to mention the feature in the review were analyzed for sentiment analysis of customers. To demonstrate the methodology, a case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment during COVID-19.

Keywords

Semantic data processingMachine learningMarket implications
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 457 - 466
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), 2021. Published by Cambridge University Press

References

Alexander, D., & Karger, E., 2020. Do stay-at-home orders cause people to stay at home? Effects of stay-at-home orders on consumer behavior.CrossRefGoogle Scholar
Aslam, F., Awan, T. M., Syed, J. H., Kashif, A., and Parveen, M., 2020. Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, 7(1), pp.1–9.CrossRefGoogle Scholar
Bag, S., Tiwari, M. K., and Chan, F. T., 2019. Predicting the consumer's purchase intention of durable goods: An attribute-level analysis. Journal of Business Research, 94, 408–419.CrossRefGoogle Scholar
Bhargava, S. et al. , 2020. Survey: US Consumer Sentiment During the Coronavirus Crisis, Mckinsey and Co., April 5, 2020. Retrieved from https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/survey-us-consumersentiment-during-the-coronavirus-crisisGoogle Scholar
Dunford, D., Dale, B., Stylianou, N., Lowther, E., Ahmed, M., and de la Torre Arenas, I., 2020. Coronavirus: The world in lockdown in maps and charts. Retrieved from https://www.bbc.com/news/world-52103747Google Scholar
Dunn, A., Hood, K., and Driessen, A., 2020. Measuring the effects of the COVID-19 pandemic on consumer spending using card transaction data. US Bureau of Economic Analysis Working Paper WP2020–5.Google Scholar
Kohli, S., Timelin, B., Fabius, V., and Veranen, S. M., 2020. How COVID-19 is changing consumer behavior–now and forever. Retrieved from https://www.mckinsey.com/industries/retail/our-insights/how-covid-19-is-changing-consumer-behavior-now-and-foreverGoogle Scholar
Krakowski, C., 2020. June 2020 Ecommerce Market Trends: Focus on Review Length + Your Questions Answered + Webinar Recording. Retrieved from https://www.powerreviews.com/blog/june-2020-ecommerce-market-trends-focus-on-review-length-your-questions-answered-webinar-recording/Google Scholar
Koshi, L., 2020. Second-hand smartphones see spike in sales amid COVID-19 pandemic, Retrieved from https://thenewsminute.com/article/second-hand-smartphones-see-spike-sales-amid-covid-19-pandemic-128363Google Scholar
Park, S., and Kim, H., 2020. Improving the accuracy and diversity of feature extraction from online reviews using keyword embedding and two clustering method. Proceeding of ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering ConferenceCrossRefGoogle Scholar
Sarkis, J., Cohen, M. J., Dewick, P., and Schröder, P., 2020. A brave new world: lessons from the COVID-19 pandemic for transitioning to sustainable supply and production. Resources, Conservation, and Recycling.CrossRefGoogle Scholar
Suryadi, D., and Kim, H., 2018. A systematic methodology based on word embedding for identifying the relation between online customer reviews and sales rank. Journal of Mechanical Design, 140(12).CrossRefGoogle Scholar
Turney, P. D., and Littman, M. L., 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4), pp.315–346.CrossRefGoogle Scholar
Tuarob, S., and Tucker, C. S., 2015. Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data. Journal of Mechanical Design, 15.Google Scholar
Vanian, J., 2020. This smartphone maker's sales dropped the most due to COVID-19. Retrieved from https://fortune.com/2020/08/25/apple-samsung-huawei-smartphone-covid-19/Google Scholar
Zhang, H., Rao, H., and Feng, J., 2018. Product innovation based on online review data mining: a case study of Huawei phones. Electronic Commerce Research, 18(1), pp.3–22.CrossRefGoogle Scholar
Zhou, F., Ayoub, J., Xu, Q., and Yang, X. J., 2020. A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems. Journal of Mechanical Design, 142.CrossRefGoogle Scholar