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Abstract
Brand knowledge is determined by customer knowledge. The opportunity to develop brands based on customer knowledge management has never been greater. Social media as a set of leading communication platforms enable peer to peer interplays between customers and brands. A large stream of such interactions is a great source of information which, when thoroughly analyzed, can become a source of innovation and lead to competitive advantage. Semantic analysis is a prominent field of data mining that deals with key contexts, topics, and sentiment of these interactions. The challenge and key to the success are creating a proper searching algorithm to analyze these areas of interest. The purpose of this study is to develop and to test a methodology which will identify principal points of customers' interactions with fashion brands using a set of Text Mining Algorithms. The fashion industry is one of the most successful in the social media environment. Deep understanding of fashion brand communication is interesting from the theoretical and practical point of view. The theoretical value of this study contributes to the social media brand knowledge management by providing a set of gained insights thanks to the implementation of the new methodological approach presented in this study. The practical value is the knowledge about the presence of fashion brands in social media obtained in the course of the study.
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- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- 31st International-Business-Information-Management-Association Conference strony 1972 - 1983
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Rizun N., Kucharska W..: Text Mining Algorithms for Extracting Brand Knowledge; The fashion Industry Case, W: 31st International-Business-Information-Management-Association Conference, 2018, International Business Information Management Association (IBIMA) ,.
- Verified by:
- Gdańsk University of Technology
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