文章基本信息

标题：Managing demand volatility during unplanned events with sentiment analysis: a case study of the COVID-19 pandemic
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作者：Angie Nguyen ; Samir Lamouri ; Robert Pellerin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：1
页码：1017-1022
DOI：10.1016/j.ifacol.2021.08.200
语种：English
出版社：Elsevier
摘要：AbstractUnplanned events such as natural disasters or epidemic outbreaks are usually accompanied by supply chain disruption and highly volatile markets. Besides, the recent COVID-19 crisis has shown that existing artificial intelligence systems and data analytics models, which normally provide valuable support in demand forecasting, have not been able to manage demand volatility. This study contributes addressing this issue and aims to determine whether sentiments conveyed by news media influence consumer behavior. It provides a case study conducted in three steps: (1) data were collected and prepared; (2) a sentiment analysis model was developed; and (3) a statistical analysis was performed to analyze the correlation between sentiments in news and drug consumption during the COVID-19 crisis. Findings highlighted a strong positive correlation between sentiments in news and consumption variability. They therefore suggest that sentiments in news have strong predictive power for demand forecasting in unplanned situations.
关键词：Keywordssentiment analysisnews mediademand forecastingcrisis managementnatural language processingepidemic outbreakanalyticsartificial intelligence