Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach

Di (Richard) Shang; Zhaoxia Wang; Zhenda Hu

doi:10.1007/978-3-030-78609-0_47

Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach

Di (Richard) Shang, Zhaoxia Wang, Zhenda Hu

Management

Research output: Chapter or Contribution to Book › Literary contribution

Abstract

There is a rich collection of studies exploring different aspects of consumer brand relationship. Traditional approaches of questionnaires and analysis are based on measurements collected from a relatively small number of survey participants. With the advancements in natural language processing (NLP) techniques, opportunities exist for applying NLP techniques to discover consumer brand relationship from social media platforms that possess a large amount of data on consumer opinion and sentiment. In this study, we review consumer brand relationship analysis focusing on leveraging NLP and machine learning techniques to address some challenges associated with discovering customer brand relationship from social media data and propose a methodological framework for the approach. This study has implications for both academic research and practitioners as it presents an alternative way to investigate consumer brand relationship.

Original language	American English
Title of host publication	Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings
Subtitle of host publication	7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I
Editors	Xingming Sun, Xiaorui Zhang, Zhihua Xia, Elisa Bertino
Place of Publication	Switzerland
Pages	533-565
Number of pages	13
ISBN (Electronic)	978-3-030-78609-0
DOIs	https://doi.org/10.1007/978-3-030-78609-0_47
State	Published - 2021
Event	7th International Conference on Artificial Intelligence and Security, ICAIS 2021 - Dublin, Ireland Duration: Jul 19 2021 → Jul 23 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12736 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Artificial Intelligence and Security, ICAIS 2021
Country/Territory	Ireland
City	Dublin
Period	7/19/21 → 7/23/21

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

NLP
Text mining
Consumer brand relationship
Machine Learning

Access to Document

10.1007/978-3-030-78609-0_47

https://link.springer.com/chapter/10.1007/978-3-030-78609-0_47

Cite this

Shang, D., Wang, Z., & Hu, Z. (2021). Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach. In X. Sun, X. Zhang, Z. Xia, & E. Bertino (Eds.), Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I (pp. 533-565). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12736 LNCS).. https://doi.org/10.1007/978-3-030-78609-0_47

Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach. / Shang, Di (Richard); Wang, Zhaoxia; Hu, Zhenda.
Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I. ed. / Xingming Sun; Xiaorui Zhang; Zhihua Xia; Elisa Bertino. Switzerland, 2021. p. 533-565 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12736 LNCS).

Research output: Chapter or Contribution to Book › Literary contribution

Shang, D, Wang, Z & Hu, Z 2021, Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach. in X Sun, X Zhang, Z Xia & E Bertino (eds), Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12736 LNCS, Switzerland, pp. 533-565, 7th International Conference on Artificial Intelligence and Security, ICAIS 2021, Dublin, Ireland, 7/19/21. https://doi.org/10.1007/978-3-030-78609-0_47

Shang D, Wang Z, Hu Z. Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach. In Sun X, Zhang X, Xia Z, Bertino E, editors, Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I. Switzerland. 2021. p. 533-565. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-78609-0_47

Shang, Di (Richard) ; Wang, Zhaoxia ; Hu, Zhenda. / Mining Consumer Brand Relationship from Social Media Data : A Natural Language Processing Approach. Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings: 7th International Conference, ICAIS 2021, Dublin, Ireland, July 19–23, 2021, Proceedings, Part I. editor / Xingming Sun ; Xiaorui Zhang ; Zhihua Xia ; Elisa Bertino. Switzerland, 2021. pp. 533-565 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "There is a rich collection of studies exploring different aspects of consumer brand relationship. Traditional approaches of questionnaires and analysis are based on measurements collected from a relatively small number of survey participants. With the advancements in natural language processing (NLP) techniques, opportunities exist for applying NLP techniques to discover consumer brand relationship from social media platforms that possess a large amount of data on consumer opinion and sentiment. In this study, we review consumer brand relationship analysis focusing on leveraging NLP and machine learning techniques to address some challenges associated with discovering customer brand relationship from social media data and propose a methodological framework for the approach. This study has implications for both academic research and practitioners as it presents an alternative way to investigate consumer brand relationship.",

keywords = "NLP, Text mining, Consumer brand relationship, Machine Learning",

author = "Shang, {Di (Richard)} and Zhaoxia Wang and Zhenda Hu",

note = "Shang, D., Hu, Z., Wang, Z. (2021). Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_47; 7th International Conference on Artificial Intelligence and Security, ICAIS 2021 ; Conference date: 19-07-2021 Through 23-07-2021",

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AB - There is a rich collection of studies exploring different aspects of consumer brand relationship. Traditional approaches of questionnaires and analysis are based on measurements collected from a relatively small number of survey participants. With the advancements in natural language processing (NLP) techniques, opportunities exist for applying NLP techniques to discover consumer brand relationship from social media platforms that possess a large amount of data on consumer opinion and sentiment. In this study, we review consumer brand relationship analysis focusing on leveraging NLP and machine learning techniques to address some challenges associated with discovering customer brand relationship from social media data and propose a methodological framework for the approach. This study has implications for both academic research and practitioners as it presents an alternative way to investigate consumer brand relationship.

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Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach

Abstract

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Conference

ASJC Scopus Subject Areas

Keywords

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