Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning

Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumers’ prior reviews can be useful for inexperienced consumers. However, one-sided review systems (e.g., Amazon) only pro...

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Autor principal:	Jikhan Jeong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Online product review consumer behavior natural language prediction machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f4b199f0dea74b9293679060095a0c14
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spelling	oai:doaj.org-article:f4b199f0dea74b9293679060095a0c142021-11-18T00:08:14ZIdentifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning2169-353610.1109/ACCESS.2021.3123301https://doaj.org/article/f4b199f0dea74b9293679060095a0c142021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9590517/https://doaj.org/toc/2169-3536Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumers’ prior reviews can be useful for inexperienced consumers. However, one-sided review systems (e.g., Amazon) only provide the opportunity for consumers to write a review as a buyer and contain no feedback from the seller’s side, so the information displayed about individual buyers is limited. Therefore, this study analyzes consumers’ digital footprints (DFs) for programmable thermostats to identify and predict unobserved consumer preferences, using a dataset of 141 million Amazon reviews. This paper proposes novel approaches (1) to identify unobserved consumer characteristics and preferences by analyzing the target consumers’ and other prior reviewers’ DFs; (2) to extract product-specific product content dimensions (PCDs) from review text data; (3) to predict individual consumers’ sentiment before they make a purchase or write a review; (4) to classify consumers’ sentiment toward a specific PCD by using context-based word embedding and deep learning models. Overall, this approach developed in this paper is applicable, scalable, and interpretable for distinguishing important drivers of consumer reviews for different goods in a specific industry and can be used by industry to design customer-oriented marketing strategies.Jikhan JeongIEEEarticleOnline product reviewconsumer behaviornatural language predictionmachine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147357-147396 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Online product review consumer behavior natural language prediction machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Online product review consumer behavior natural language prediction machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jikhan Jeong Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning
description	Inexperienced consumers may have high uncertainty about experience goods that require technical knowledge and skills to operate effectively; therefore, experienced consumers’ prior reviews can be useful for inexperienced consumers. However, one-sided review systems (e.g., Amazon) only provide the opportunity for consumers to write a review as a buyer and contain no feedback from the seller’s side, so the information displayed about individual buyers is limited. Therefore, this study analyzes consumers’ digital footprints (DFs) for programmable thermostats to identify and predict unobserved consumer preferences, using a dataset of 141 million Amazon reviews. This paper proposes novel approaches (1) to identify unobserved consumer characteristics and preferences by analyzing the target consumers’ and other prior reviewers’ DFs; (2) to extract product-specific product content dimensions (PCDs) from review text data; (3) to predict individual consumers’ sentiment before they make a purchase or write a review; (4) to classify consumers’ sentiment toward a specific PCD by using context-based word embedding and deep learning models. Overall, this approach developed in this paper is applicable, scalable, and interpretable for distinguishing important drivers of consumer reviews for different goods in a specific industry and can be used by industry to design customer-oriented marketing strategies.
format	article
author	Jikhan Jeong
author_facet	Jikhan Jeong
author_sort	Jikhan Jeong
title	Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning
title_short	Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning
title_full	Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning
title_fullStr	Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning
title_full_unstemmed	Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning
title_sort	identifying consumer preferences from user-generated content on amazon.com by leveraging machine learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f4b199f0dea74b9293679060095a0c14
work_keys_str_mv	AT jikhanjeong identifyingconsumerpreferencesfromusergeneratedcontentonamazoncombyleveragingmachinelearning
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Identifying Consumer Preferences From User-Generated Content on Amazon.Com by Leveraging Machine Learning

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