Geo-semantic profiling of brand-specific customer experience using citizen-generated social media comments

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dc.contributor.authorNgele, E.
dc.date.accessioned2024-11-05T12:25:09Z
dc.date.available2024-11-05T12:25:09Z
dc.date.issued2024-10-28
dc.descriptionScholarly article
dc.description.abstractA good customer experience is likely to influence a customer’s decision to buy positively and equally a negative customer experience will most likely make a customer decide not to buy or go elsewhere. About 73% of customers said that one negative customer experience is enough for them to leave a brand, and go to a competitor. This research work will use text mining and sentiment analysis techniques to identify and categorize brand-specific social media comments into different groups, including positive, negative, and neutral comments. Geo-profiling techniques will be used to identify patterns and trends in customer feedback from different locations. The results will help businesses to understand how to use social media platforms effectively to collect and analyze customer feedback to improve their customer experience and also aid in providing tailored services and products to their customers based on their geographic location. This research will also contribute to the field of customer relationship management by providing new insights into how businesses can use geo-profiling techniques to enhance customer loyalty and satisfaction.
dc.identifier.citationEmmanuel N. (2023). “Geo-semantic profiling of brand-specific customer experience using citizen-generated social media comments". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2023), pp. 149-153, MIRG
dc.identifier.isbn978-978-767-697-4
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13035
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2023
dc.titleGeo-semantic profiling of brand-specific customer experience using citizen-generated social media comments
dc.typeArticle
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