Analysis of Dynamic Changes in Customer Sentiment on Product Features After the Outbreak of COVID-19 Based on Online Reviews

被引:3
|
作者
Kim, Jinju [1 ]
Park, Seyoung [1 ]
Kim, Harrison M. [1 ]
机构
[1] Univ Illinois, Dept Ind & Enterprise Syst Engn, Enterprise Syst Optimizat Lab, Urbana, IL 61801 USA
关键词
data-driven design; online customer analysis;
D O I
10.1115/1.4052789
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Sudden changes in life and work patterns due to COVID-19 have affected customer requirements for the products. Under these circumstances, companies can achieve high profitability and customer satisfaction when they can efficiently identify and respond quickly to changing customer preferences caused by COVID-19. This article presents empirical research on dynamic changes in customer responses for product features caused by the spread of COVID-19 through sentiment analysis based on online reviews. A case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment before/during COVID-19. The importance of the result is shown by comparing it to the actual market data.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Impact of Social Sentiment Changes on People's Consumption Behavior during COVID-19 Outbreak
    Zhou, Jianlong
    Yao, Xiangyu
    Miao, Xianglin
    He, Feijuan
    Miao, Yalin
    2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA, 2023, : 113 - 119
  • [42] Changes in air pollution levels after COVID-19 outbreak in Korea
    Ju, Min Jae
    Oh, Jaehyun
    Choi, Yoon-Hyeong
    SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 750
  • [43] Sentiment analysis of restaurant customer satisfaction during COVID-19 pandemic in Pattaya, Thailand
    Pleerux, Narong
    Nardkulpat, Attawut
    HELIYON, 2023, 9 (11)
  • [44] Changes in air pollution levels after COVID-19 outbreak in Korea
    Ju, Min Jae
    Oh, Jaehyun
    Choi, Yoon-Hyeong
    Science of the Total Environment, 2021, 750
  • [45] Customer preferences extraction for air purifiers based on fine-grained sentiment analysis of online reviews
    Zhang, Jing
    Zhang, Aijia
    Liu, Dian
    Bian, Yiwen
    KNOWLEDGE-BASED SYSTEMS, 2021, 228
  • [46] The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis
    De Rosis, Sabina
    Lopreite, Milena
    Puliga, Michelangelo
    Vainieri, Milena
    HEALTH POLICY, 2021, 125 (08) : 987 - 994
  • [47] Sentiment analysis of online product reviews using Lexical Semantic Corpus-Based technique
    Aminuddin, Raihah
    Zulkefli, Aina Zuliana
    Moketar, Nor Aiza
    Abu Samah, Khyrina Airin Fariza
    11TH IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE 2021), 2021, : 233 - 238
  • [48] A Dynamic Product Evaluation Model Based on Online Customer Reviews from the Perspective of the Elaboration Likelihood Model
    Li, Yang
    Xu, Zeshui
    Zhang, Yixin
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2023, 2023
  • [49] What is the impact of service quality on customers' satisfaction during COVID-19 outbreak? New findings from online reviews analysis
    Nilashi, Mehrbakhsh
    Abumalloh, Rabab Ali
    Alghamdi, Abdullah
    Minaei-Bidgoli, Behrouz
    Alsulami, Abdulaziz A.
    Thanoon, Mohammed
    Asadi, Shahla
    Samad, Sarminah
    TELEMATICS AND INFORMATICS, 2021, 64
  • [50] Comparative Analysis of COVID-19 Outbreak and Changes in Neurosurgical Emergency Patients
    Lee, Min Ho
    Jang, Seu-Ryang
    Lee, Tae-Kyu
    JOURNAL OF KOREAN NEUROSURGICAL SOCIETY, 2022, 65 (01) : 130 - 137