Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics
Lecture Notes on Data Engineering and Communications Technologies
; 165:209-221, 2023.
Article
in English
| Scopus | ID: covidwho-2300583
ABSTRACT
Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Consumer behavior; Covid-19 pandemic; Machine learning; Recommendation systems; Sentiment analysis; Social media; Forecasting; Online systems; Sales; Social networking (online); Behavior prediction; Condition; Consumer purchase; Customer behavior; Global shift; Machine-learning; Media usage; Recommender systems
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
Lecture Notes on Data Engineering and Communications Technologies
Year:
2023
Document Type:
Article
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