Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach
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.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Digital Object Identifier (DOI)
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