Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Saravanan Murugan; Sulaf Assi; Abbas Alatrany; Manoj Jayabalan; Panagiotis Liatsis; Jamila Mustafina; Abdullah Al-Hamid; Maitham G. Yousif; Ahmed Kaky; Danny Ngo Lung Yao; Dhiya Al-Jumeily OBE

doi:10.1007/978-981-99-0741-0_15

Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Saravanan Murugan, Sulaf Assi, Abbas Alatrany, Manoj Jayabalan, Panagiotis Liatsis, Jamila Mustafina, Abdullah Al-Hamid, Maitham G. Yousif, Ahmed Kaky, Danny Ngo Lung Yao, Dhiya Al-Jumeily OBE

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

7 Scopus citations

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.

Original language	British English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	209-221
Number of pages	13
DOIs	https://doi.org/10.1007/978-981-99-0741-0_15
State	Published - 2023

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	165
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Keywords

Consumer behavior
Covid-19 pandemic
Machine learning
Recommendation systems
Sentiment analysis
Social media

Access to Document

10.1007/978-981-99-0741-0_15

Cite this

Murugan, S., Assi, S., Alatrany, A., Jayabalan, M., Liatsis, P., Mustafina, J., Al-Hamid, A., Yousif, M. G., Kaky, A., Yao, D. N. L., & Al-Jumeily OBE, D. (2023). Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics. In Lecture Notes on Data Engineering and Communications Technologies (pp. 209-221). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 165). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-0741-0_15

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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.",

keywords = "Consumer behavior, Covid-19 pandemic, Machine learning, Recommendation systems, Sentiment analysis, Social media",

author = "Saravanan Murugan and Sulaf Assi and Abbas Alatrany and Manoj Jayabalan and Panagiotis Liatsis and Jamila Mustafina and Abdullah Al-Hamid and Yousif, {Maitham G.} and Ahmed Kaky and Yao, {Danny Ngo Lung} and {Al-Jumeily OBE}, Dhiya",

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doi = "10.1007/978-981-99-0741-0_15",

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Murugan, S, Assi, S, Alatrany, A, Jayabalan, M, Liatsis, P, Mustafina, J, Al-Hamid, A, Yousif, MG, Kaky, A, Yao, DNL & Al-Jumeily OBE, D 2023, Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 165, Springer Science and Business Media Deutschland GmbH, pp. 209-221. https://doi.org/10.1007/978-981-99-0741-0_15

Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics. / Murugan, Saravanan; Assi, Sulaf; Alatrany, Abbas et al.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2023. p. 209-221 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 165).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Murugan S, Assi S, Alatrany A, Jayabalan M, Liatsis P, Mustafina J et al. Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics. In Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH. 2023. p. 209-221. (Lecture Notes on Data Engineering and Communications Technologies). doi: 10.1007/978-981-99-0741-0_15

Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

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