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Determining Brand Personality in Tourism Businesses Through Sentiment Analysis Method

Yıl 2023, Cilt: 16 Sayı: 1, 229 - 254, 30.06.2023
https://doi.org/10.37093/ijsi.1279606

Öz

The rapid developments in information technologies have provided people with the internet environment where they can share their ideas and feedback about businesses in an unlimited and fast way. When examined from the perspective of businesses, this highlights the necessity for quick reactions to customers' rapid feedback. This fast interaction channel between businesses and consumers triggers a new type of work when it becomes meaningful. Businesses attempt to detect what people think about them through online comments and sometimes even virtual behaviors. All of these factors that increase the value of data create new working topics for how businesses can process and interpret their data. It shows that if businesses can collect their customers' opinions more quickly than with traditional methods, process them faster, and make decisions more quickly, they can gain significant advantages over their competitors. In this study, sentiment analysis method, which has been frequently used in recent years, is divided into sub-dimensions that are different from the dimensions analyzed before, with a different perspective. The study aims to be able to tell which brand personality customers attribute to businesses within sub-dimensions that can serve tourism businesses, using a supervised learning dataset, by separating negative comments and only analyzing positive ones.

Kaynakça

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  • Ahmad, A., Swain, S., Singh, P. K., Yadav, R., & Prakash, G. (2021). Linking brand personality to brand equity: Measuring the role of consumer-brand relationship. Journal of Indian Business Research, 13(4), 586–602. https://doi.org/10.1108/JIBR-01-2021-0017
  • Aimé, I., Berger-Remy, F., & Laporte, M.-E. (2022). The brand, the persona and the algorithm: How datafication is reconfiguring marketing work. Journal of Business Research, 145, 814–827. https://doi.org/10.1016/j.jbusres.2022.03.047
  • D’Andrea, A., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3), 26–33. https://doi.org/10.5120/ijca2015905866
  • Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Management Journal, 27(4), 1108–1125. https://doi.org/10.1108/bpmj-11-2020-0527
  • Alsghaier, H., Akour, M., Shehabat, I., & Aldiabat, S. (2017). The Importance of big data analytics in business: A case study. American Journal of Software Engineering and Applications, 6(4), 111-115. https://doi.org/10.11648/j.ajsea.20170604.12
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  • Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), 1993–2009. https://doi.org/10.1108/md-07-2018-0754
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Turizm İşletmelerinde Marka Kişiliğinin Duygu Analizi Yöntemiyle Belirlenmesi

Yıl 2023, Cilt: 16 Sayı: 1, 229 - 254, 30.06.2023
https://doi.org/10.37093/ijsi.1279606

Öz

Bilişim teknolojilerindeki hızlı gelişmeler, insanların işletmelere karşı fikirlerini sınırsızca ve hızlı bir şekilde yayabildiği internet ortamını insanların hizmetine çok geniş bir çerçevede sunmaktadır. İşletmelerin bakış açısından incelendiğinde ise, müşterilerin bu hızlı geri bildirimlerine verilecek tepkilerin de hızlı olması gerekliliğini ortaya koymaktadır. İşletme ile tüketici arasında oluşan bu hızlı etkileşim kanalı, anlam ifade eder hale geldiği noktada yeni bir çalışmayı tetiklemektedir. İşletmeler hakkında neler düşünüldüğü, internet üzerindeki yorumlardan hatta bazen sanal ortamdaki davranışlardan tespit edilmeye çalışılmaktadır. Verinin değerini artıran tüm bu olgular, işletmeler içinde sahip oldukları verileri nasıl işleyecekleri ve işledikten sonra bunları nasıl anlamlandırmaları gerektiği konularında yepyeni çalışma başlıkları açmaktadır. Müşterilerin işletmeler hakkındaki düşüncelerini eski yöntemlere göre daha hızlı toplayıp, daha hızlı işleyip daha hızlı kararlar verebilirlerse rakiplerine göre önemli avantajlar elde edebileceğini göstermektedir. Bu çalışmada, son yıllarda sıkça başvurulan duygu analizi yöntemi, farklı bir bakış açısıyla, şimdiye kadar yapılmış analiz boyutlarından daha farklı şekilde alt boyutlara ayrılarak yapılmaktadır. Çalışmada denetimli öğrenme yapabilen bir veri seti, turizm işletmelerine hizmet edebilecek alt boyutlar kapsamında, olumsuz yorumlar bir kenara ayrılarak sadece olumlu yorumlar içerisinde, müşterilerin onlara hangi marka kişiliğini atfettiğini söyleyebilmeyi hedeflemektedir.

Kaynakça

  • Aaker, J. L. (1997). Dimensions of brand personality. Journal of Marketing Research, 34(3), 347–356. https://doi.org/10.1177/002224379703400304
  • Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U., Franklin, M., Gehrke, J., Haas, L., Halevy, A., Han, J., Jagadish, H. V., Labrinidis, A., Madden, S., Papakonstantinou, Y., Patel, J., Ramakrishnan, R., Ross, K., Shahabi, C., Suciu, D., … Widom, J. (2011). Challenges and Opportunities with Big Data 2011-1. Cyber Center Technical Reports. https://docs.lib.purdue.edu/cctech/1
  • Ahmad, A., & Thyagaraj, K. (2014). Applicability of brand personality dimensions across cultures and product categories: A review. Global Journal of Finance and Management, 6(1), 9-18. https://www.ripublication.com/gjfm-spl/gjfmv6n1_02.pdf
  • Ahmad, A., Swain, S., Singh, P. K., Yadav, R., & Prakash, G. (2021). Linking brand personality to brand equity: Measuring the role of consumer-brand relationship. Journal of Indian Business Research, 13(4), 586–602. https://doi.org/10.1108/JIBR-01-2021-0017
  • Aimé, I., Berger-Remy, F., & Laporte, M.-E. (2022). The brand, the persona and the algorithm: How datafication is reconfiguring marketing work. Journal of Business Research, 145, 814–827. https://doi.org/10.1016/j.jbusres.2022.03.047
  • D’Andrea, A., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3), 26–33. https://doi.org/10.5120/ijca2015905866
  • Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Management Journal, 27(4), 1108–1125. https://doi.org/10.1108/bpmj-11-2020-0527
  • Alsghaier, H., Akour, M., Shehabat, I., & Aldiabat, S. (2017). The Importance of big data analytics in business: A case study. American Journal of Software Engineering and Applications, 6(4), 111-115. https://doi.org/10.11648/j.ajsea.20170604.12
  • Alt, M., & Griggs, S. (1988). Can a brand be cheeky? Marketing Intelligence & Planning, 6(4), 9–16. https://doi.org/10.1108/eb045776
  • American Psychological Association (2022). Personality. Retrieved May 22, 2022, from https://www.apa.org/topics/personality
  • Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94–101. https://doi.org/10.1016/j.aci.2018.05.004
  • Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), 1993–2009. https://doi.org/10.1108/md-07-2018-0754
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  • Grishikashvili, K., Dibb, S., & Meadows, M. (2014). Investigation into big data impact on digital marketing. Online Journal of Communication and Media Technologies, 4(October 2014-Special Issue), 26–37. https://doi.org/10.30935/ojcmt/5702
  • Gursoy, U. T., Bulut, D., & Yigit, C. (2017). Social media mining and sentiment analysis for brand management. Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology, 3(1), 497-551.
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  • Kim, H. R., Lee, M., & Ulgado, F. M. (2005). Brand personality, self-congruity and the consumer-brand relationship. ACR Asia-Pacific Advances.
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  • Kopalle, P. K., & Lehmann, D. R. (2021). Big Data, Marketing Analytics, and Public Policy: Implications for Health Care. Journal of Public Policy & Marketing, 40(4), 453–456. https://doi.org/10.1177/0743915621999031
  • Lehmann, D. R., Keller, K. L., & Farley, J. U. (2008). The structure of survey-based brand metrics. Journal of International Marketing, 16(4), 29–56. https://doi.org/10.1509/jimk.16.4.29
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  • Mariani, M., & Borghi, M. (2020). Environmental discourse in hotel online reviews: a big data analysis. Journal of Sustainable Tourism, 29(5), 829–848. https://doi.org/10.1080/09669582.2020.1858303
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  • Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems With Applications, 40(10), 4241–4251. https://doi.org/10.1016/j.eswa.2013.01.019
  • Mostafa, M. M. (2019). Clustering halal food consumers: A Twitter sentiment analysis. International Journal of Market Research, 61(3), 320–337. https://doi.org/10.1177/1470785318771451
  • Mowlaei, M. E., Saniee Abadeh, M., & Keshavarz, H. (2020). Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Systems With Applications, 148, 113234. https://doi.org/10.1016/j.eswa.2020.113234
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  • Pinarbasi, F., & Canbolat, Z. N. (2019). Big data in marketing literature. International Journal of Business Ecosystem & Strategy (2687-2293), 1(2), 15–24. https://doi.org/10.36096/ijbes.v1i2.107
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  • Rogers, D., & Sexton, D. (2012). Marketing ROI in the Era of big data: The 2012 BRITE/NYAMA marketing in transition study. Columbia Business School, New York.
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  • Siddharth, S., Darsini, R., & Sujithra, M. (2018). Sentiment analysis on twitter data using machine learning algorithms in python. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(2), 285-291.
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Toplam 85 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Büyük ve Karmaşık Veri Teorisi, Ürün ve Marka Yönetimi
Bölüm Makaleler
Yazarlar

Nebi Seren 0000-0003-4080-4823

Murat Hakan Altıntaş 0000-0001-8517-0540

Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 8 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 16 Sayı: 1

Kaynak Göster

APA Seren, N., & Altıntaş, M. H. (2023). Turizm İşletmelerinde Marka Kişiliğinin Duygu Analizi Yöntemiyle Belirlenmesi. International Journal of Social Inquiry, 16(1), 229-254. https://doi.org/10.37093/ijsi.1279606

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