Abstract
Relevant branding projects start with a significant name, so the naming process is essential for creating a successful brand. However, not everyone has access to branding experts to guide this naming process. This paper proposes an accessible solution that makes a technical evaluation of brand names, supporting and helping people to decide the best name for their business. For this, our work aims to develop a mobile application that uses machine learning models trained on a database created by naming experts that evaluates many brand names accordingly to ten technical criteria. Thus, we provide a mobile application that makes the machine learning models accessible to the user in a simple way, allowing him/her to make rational decisions based on technical knowledge. Furthermore, we demonstrate the accuracy of our models on predicting the evaluation of the brand names. For the vast majority of the evaluation criteria, our application automatically generated a score matching with the output given by the branding experts within a positive/negative deviation of one point in more than 80% of the cases. Finally, an MVP is developed that already presents satisfactory results.
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Notes
- 1.
Available in: https://keras.io/.
- 2.
Available in: https://flutter.dev/.
- 3.
Available in: https://www.tensorflow.org/.
References
Chiranjeeb, K., Doglas, W.L.: Observations: creating effective brand name: a study of the naming process. J. Advert. Res. 37(1), 67–75 (1997)
Bhat, S., Kelley, G., O’Donnell, K.: An investigation of consumer reactions to the use of different brand names. J. Prod. Brand Manag. 7, 41–50 (1998)
Ruffell, B.: The genetics of brandnaming. J. Brand Manag. 4, 10 (1996)
Rubio, N., Villaseñor, N., Yagüe, M.: The role of private label tiers and private label naming strategies in the relationship between private label brand equity and store loyalty. J. Prod. Brand Manag. 29(1), 124–138 (2019)
Choi, L., Huddleston, P.: The effect of retailer private brands on consumer-based retailer equity: comparison of named private brands and generic private brands. Int. Rev. Retail Distrib. Consum. Res. 24, 59–78 (2014)
Martinez, J.: The magic of ‘great’ linked to product names. J. Brand Manag. 23, 03 (2016)
Kara, S., Gunasti, Ross, W.T.: My brand identity lies in the brand name: personified suggestive brand names. J. Brand Manag. 27, 09 (2020)
Arora, S., Kalro, A., Sharma, D.: A comprehensive framework of brand name classification. J. Brand Manag. 22, 05 (2015)
Andreas, B., Eisingerich, C.: Whan Park, Deborah J. MacInnis. Brand Architecture Design and Brand Naming Decisions. Routledge, London (2016)
Kenagy, J.W., Stein, G.C.: Naming, labeling, and packaging of pharmaceuticals. Am. J. Health-Syst. Pharm. 58(21), 2033–2041 (2001)
Undefinedniegula, A., Poniszewska-Marańda, A., Popović, M.: Study of machine learning methods for customer churn prediction in telecommunication company. In: Proceedings of the 21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS2019, New York, NY, USA, pp. 640–644. Association for Computing Machinery (2019)
Tomihira, T., Otsuka, A., Yamashita, A., Satoh, T.: What does your tweet emotion mean? Neural emoji prediction for sentiment analysis. In: Proceedings of the 20th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2018, New York, NY, USA, pp. 289–296. Association for Computing Machinery (2018)
Eddamiri, S., Zemmouri, E., Benghabrit, A.: Graph embeddings for linked data clustering. In: Proceedings of the 20th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2018, New York, NY, USA, pp. 122–128. Association for Computing Machinery (2018)
Grzegorczyk, K.: Vector representations of text data in deep learning. CoRR, abs/1901.01695 (2019)
Liu, N., et al.: Text representation: from vector to tensor. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), p. 4 (2005)
Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: Proceedings of the Seventh International Conference on Information and Knowledge Management, CIKM 1998, New York, NY, USA, pp. 148–155. Association for Computing Machinery (1998)
Grefenstette, G.: Tokenization, pp. 117–133. Springer, Dordrecht (1999). https://doi.org/10.1007/978-94-015-9273-4_9
Mohan, V.: Text mining: open source tokenization tools: an analysis. Adv. Comput. Intell. Int. J. (ACII) 3, 37–47 (2016)
Mullen, L.A., Benoit, K., Keyes, O., Selivanov, D., Arnold, J.: Fast, consistent tokenization of natural language text. J. Open Source Softw. 3, 655 (2018)
Pham, D.-H., Le, A.-C.: Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis. Int. J. Approx. Reason. 103, 1–10 (2018)
El Affendi, M.A., Al Rajhi, K.H.S.: Text encoding for deep learning neural networks: a reversible base 64 (tetrasexagesimal) integer transformation (rit64) alternative to one hot encoding with applications to Arabic morphology. In: 2018 Sixth International Conference on Digital Information, Networking, and Wireless Communications (DINWC), pp. 70–74 (2018)
Kumar, N., Bhatia, M.S., Aggarwal, A.: Speech-to-text conversion using GRU and one hot vector encodings. PalArch’s J. Archaeol. Egypt/Egyptol. 17(10), 7110–7119 (2020)
Khrulkov, V., Hrinchuk, O., Mirvakhabova, L., Oseledets, I.V.: Tensorized embedding layers for efficient model compression. CoRR, abs/1901.10787 (2019)
Hrinchuk, O., Khrulkov, V., Mirvakhabova, L., Orlova, E., Oseledets, I.: Tensorized embedding layers. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4847–4860, Online. Association for Computational Linguistics, November 2020
Simanjuntak, D.A., Ipung, H.P., Lim, C., Nugroho, A.S.: Text classification techniques used to facilitate cyber terrorism investigation. In: 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 198–200 (2010)
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 7370–7377 (2019)
Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 10(4) (2019)
Alcamo, T., Cuzzocrea, A., Bosco, G.L., Pilato, G., Schicchi, D.: Analysis and comparison of deep learning networks for supporting sentiment mining in text corpora. In: Proceedings of the 22nd International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2020, New York. Association for Computing Machinery (2020)
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Vieira, J., Rocha, R., Pereira, L.F., Vanderlei, I., Araujo, J., Dantas, J. (2022). bNaming: An Intelligent Application to Assist Brand Names Definition. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_6
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