Dijana VukoviΔ – University of North, Jurja KriΕΎaniΔa 31b, 42000 VaraΕΎdin, Croatia
Sara SlamiΔ Tarade – Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia
Keywords:
Brand significance;
Brand value;
Natural Language Processing;
Topic modeling;
Semantic Brand Score
Abstract: Determining the value of brands and comparing them by anaΒlyzing key elements such as identity, image and value is important for marΒketing measures and the branding of products or services. This paper preΒsents research findings based on innovative methods for determining the significance and value of sports footwear brands. Natural Language ProΒcessing (NLP) techniques are used to analyze extensive text content collectΒed from sports footwear-related websites. To determine the most relevant sports footwear brands, NLP techniques are used for topic modeling based on the Latent Dirichlet Allocation (LDA) method. LDA is an unsupervised method used to determine the topics addressed in the analyzed texts by exΒtracting the most significant words in these topics. The importance of these identified brands in the text corpus is determined using the Semantic Brand Scores method, which uses graph theory to determine the importance of the brand in the text corpus based on three dimensions: prevalence, diversity and connectivity.
Download file
LIMEN Conference
9th International Scientific-Business Conference – LIMEN 2023 – Leadership, Innovation, Management and Economics: Integrated Politics of Research – CONFERENCE PROCEEDINGS, Hybrid (Graz University of Technology, Graz, Austria), December 7, 2023
LIMEN Conference Proceedings published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia
LIMEN Conference 2023 Conference Proceedings: ISBN 978-86-80194-78-3, ISSN 2683-6149, DOI: https://doi.org/10.31410/LIMEN.2023
Creative Commons NonΒ Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.Β
Suggested citation
VukoviΔ, D., & SlamiΔ Tarade, S. (2023). Investigating the Value of Sports Footwear Brands using Natural Language Processing Methods. In V. Bevanda (Ed.), International Scientific-Business Conference – LIMEN 2023: Vol 9. Conference Proceedings (pp. 105-114). Association of Economists and Managers of the Balkans. https://doi.org/10.31410/LIMEN.2023.105
References
Aaker, D. A. (1996). Building strong brands. New York: The Free Press, ISBN-0-02-900151-X, pp. 7β25.
Balakrishna, V. K. (1997). Theory And Problems of Graph Theory. The McGraw-Hill. CompaΒnies, Inc., ISBN 0-07-005489-4.
Beysolow, T. II. (2018). Applied Natural Language Processing with Python. Publisher Apress, ISBN-13 978-1-4842-3733-5.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). βLatent Dirichlet Allocationβ. Journal of Machine Learning Research. 3 (4β5): pp. 993β1022. doi:10.1162/jmlr.2003.3.4-5.993, 12.6.2020
Colladon, F. A. (2018). The Semantic Brand Score. Journal of Business Research, 88 (SeptemΒber 2017), 150 160. https://doi.org/10.1016/j.jbusres.2018.03.026Β
Colladon, F. A. (2019). Calculating the Semantic Brand Score with Python. Published in Towards Data Science, https://towardsdatascience.com/calculating-the-semanticbrand-score-with-pyΒthon-3f94fb8372a6Β
Keller, L. K. (2013). Strategic brand management (4th ed.), Pearson Education Limited, ISBN: 13: 978-0-273-77941-4.
Nitin, H. (2015). NLTK Essentials. Packt Publishing Ltd., ISBN 978-1-78439-690-9.
Sarkar, D. (2016). Text Analytics with Python: A Practical Real-World Approach to Gaining AcΒtionable Insights from Your Data, Apress, ISBN-13 978-1-4842-2388-8.
