Determining Brand Personality in Tourism Businesses Through Sentiment Analysis Method

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.

Anahtar Kelimeler

• sentiment analysis
• brand personality
• big data

Turizm İşletmelerinde Marka Kişiliğinin Duygu Analizi Yöntemiyle Belirlenmesi

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.

Anahtar Kelimeler

• duygu analizi
• marka kişiliği
• büyük veri

Kaynakça

1. Aaker, J. L. (1997). Dimensions of brand personality. Journal of Marketing Research, 34(3), 347–356. https://doi.org/10.1177/002224379703400304
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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

9. Alt, M., & Griggs, S. (1988). Can a brand be cheeky? Marketing Intelligence & Planning, 6(4), 9–16. https://doi.org/10.1108/eb045776
10. American Psychological Association (2022). Personality. Retrieved May 22, 2022, from https://www.apa.org/topics/personality
11. 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
12. 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
13. Arora, R., & Stoner, C. (2009). A mixed method approach to understanding brand personality. Journal of Product & Brand Management, 18(4), 272–283. https://doi.org/10.1108/10610420910972792 Azoulay, A., & Kapferer, J. N. (2003). Do brand personality scales really measure brand personality? Journal of Brand Management, 11(2), 143–155. https://doi.org/10.1057/palgrave.bm.2540162
14. Bergner, R. M. (2020). What is personality? Two myths and a definition. New Ideas in Psychology, 57, 100759. https://doi.org/10.1016/j.newideapsych.2019.100759
15. Chiang, L. L. L., & Yang, C. S. (2018). Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach. Technological Forecasting and Social Change, 130, 177–187. https://doi.org/10.1016/j.techfore.2017.06.034
16. Chung, C. J., Rhee, Y., & Cha, H. (2020). Big data analyses of Korea’s Nation branding on Google and Facebook. Korea Observer - Institute of Korean Studies, 51(1), 151–174. https://doi.org/10.29152/koiks.2020.51.1.151
17. Cowie, R., Cox, C., Martin, J. C., Batliner, A., Heylen, D., & Karpouzis, K. (2011). Issues in data labelling. In Emotion-oriented systems (pp. 213-241). Springer, Berlin, Heidelberg.
18. Davies, G., Rojas-Méndez, J. I., Whelan, S., Mete, M., & Loo, T. (2018). Brand personality: Theory and dimensionality. Journal of Product & Brand Management, 27(2), 115–127. https://doi.org/10.1108/jpbm-06-2017-1499
19. Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
20. Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
21. Freling, T. H., & Forbes, L. P. (2005). An empirical analysis of the brand personality effect. Journal of Product & Brand Management, 14(7), 404–413. https://doi.org/10.1108/10610420510633350
22. Freling, T. H., Crosno, J. L., & Henard, D. H. (2011). Brand personality appeal: conceptualization and empirical validation. Journal of the Academy of Marketing Science, 39(3), 392–406. https://doi.org/10.1007/s11747-010-0208-3
23. George, J., & Anandkumar, V. (2012). From Aaker to Heere: A review and comparison of brand personality scales. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2061570
24. Geuens, M., Weijters, B., & De Wulf, K. (2009). A new measure of brand personality. International Journal of Research in Marketing, 26(2), 97–107. https://doi.org/10.1016/j.ijresmar.2008.12.002
25. Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems With Applications, 40(16), 6266–6282. https://doi.org/10.1016/j.eswa.2013.05.057
26. Giglio, S., Pantano, E., Bilotta, E., & Melewar, T. (2020). Branding luxury hotels: Evidence from the analysis of consumers’ “big” visual data on TripAdvisor. Journal of Business Research, 119, 495–501. https://doi.org/10.1016/j.jbusres.2019.10.053
27. 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
28. 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.
29. Hassey, R. V. (2019). How brand personality and failure-type shape consumer forgiveness. Journal of Product & Brand Management, 28(2), 300–315. https://doi.org/10.1108/jpbm-09-2017-1563
30. Hernán, M. A., & Robins, J. M. (2016). Using big data to emulate a target trial when a randomized trial is not available. American Journal of Epidemiology, 183(8), 758–764. https://doi.org/10.1093/aje/kwv254
31. Ibrahim, N. F., & Wang, X. (2019). Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96, 32–45. https://doi.org/10.1016/j.chb.2019.02.004
32. Ivens, B., & Valta, K. S. (2012). Customer brand personality perception: A taxonomic analysis. Journal of Marketing Management, 28(9–10), 1062–1093. https://doi.org/10.1080/0267257x.2011.615149
33. Jardim, S., & Mora, C. (2022). Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products development or positioning. Procedia Computer Science, 196, 199–206. https://doi.org/10.1016/j.procs.2021.12.006
34. Jensen, A. R. (1958). The Maudsley personality inventory. Acta Psychologica, 14, 314–325. https://doi.org/10.1016/0001-6918(58)90023-4
35. Jiang, Q., Chen, L., Xu, R., Ao, X., & Yang, M. (2019, November). A challenge dataset and effective models for aspect-based sentiment analysis. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 6280-6285).
36. Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In 2013 Sixth International Conference on Contemporary Computing (IC3) (pp. 404-409). IEEE.
37. Khan, M. T., Durrani, M., Ali, A., Inayat, I., Khalid, S., & Khan, K. H. (2016). Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling, 4(1), 1-19. https://doi.org/10.1186/s40294-016-0016-9
38. Khanra, S., Dhir, A., Islam, A. K. M. N., & Mäntymäki, M. (2020). Big data analytics in healthcare: a systematic literature review. Enterprise Information Systems, 14(7), 878–912. https://doi.org/10.1080/17517575.2020.1812005
39. Kim, H. R., Lee, M., & Ulgado, F. M. (2005). Brand personality, self-congruity and the consumer-brand relationship. ACR Asia-Pacific Advances.
40. Kim, J., & Lim, C. (2021). Customer complaints monitoring with customer review data analytics: An integrated method of sentiment and statistical process control analyses. Advanced Engineering Informatics, 49, 101304. https://doi.org/10.1016/j.aei.2021.101304
41. 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
42. 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
43. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/s00416ed1v01y201204hlt016
44. Mannering, F., Bhat, C. R., Shankar, V., & Abdel-Aty, M. (2020). Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Analytic Methods in Accident Research, 25, 100113. https://doi.org/10.1016/j.amar.2020.100113
45. 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
46. Mayer, J. D. (2007). The big questions of personality psychology: Defining common pursuits of the discipline. Imagination, Cognition and Personality, 27(1), 3–26. https://doi.org/10.2190/ic.27.1.b
47. Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
48. Misirlis, N., & Vlachopoulou, M. (2018). Social media metrics and analytics in marketing – S3M: A mapping literature review. International Journal of Information Management, 38(1), 270–276. https://doi.org/10.1016/j.ijinfomgt.2017.10.005
49. Morgan, A., Wilk, V., Sibson, R., & Willson, G. (2021). Sport event and destination co-branding: Analysis of social media sentiment in an international, professional sport event crisis. Tourism Management Perspectives, 39, 100848. https://doi.org/10.1016/j.tmp.2021.100848
50. 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
51. 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
52. 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
53. Mulyanegara, R. C., Tsarenko, Y., & Anderson, A. (2009). The Big Five and brand personality: Investigating the impact of consumer personality on preferences towards particular brand personality. Journal of Brand Management, 16(4), 234–247. https://doi.org/10.1057/palgrave.bm.2550093
54. Munoz, T., & Kumar, S. (2004). Brand metrics: Gauging and linking brands with business performance. Journal of Brand Management, 11(5), 381–387. https://doi.org/10.1057/palgrave.bm.2540183
55. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
56. Nobanee, H. (2021). A bibliometric review of big data in finance. Big Data, 9(2), 73–78. https://doi.org/10.1089/big.2021.29044.edi
57. Pamuksuz, U., Yun, J. T., & Humphreys, A. (2021). A brand-new look at you: Predicting brand personality in social media networks with machine learning. Journal of Interactive Marketing, 56, 55–69. https://doi.org/10.1016/j.intmar.2021.05.001
58. Pavlopoulos, I. (2014). Aspect based sentiment analysis [Doctoral dissertation, Athens University of Economics and Business]. http://nlp.cs.aueb.gr/theses/ipavlopoulos-thesis.pdf
59. Piccarozzi, M., & Aquilani, B. (2022). The role of Big Data in the business challenge of Covid-19: A systematic literature review in managerial studies. Procedia Computer Science, 200, 1746–1755. https://doi.org/10.1016/j.procs.2022.01.375
60. 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
61. Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4), 3-13.
62. Rejeb, A., Rejeb, K., & Keogh, J. G. (2020). Potential of big data for marketing: A literature review. Management Research and Practice, 12(3), 60-73.
63. 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.
64. Saidali, J., Rahich, H., Tabaa, Y., & Medouri, A. (2019). The combination between Big Data and Marketing Strategies to gain valuable Business Insights for better Production Success. Procedia Manufacturing, 32, 1017–1023. https://doi.org/10.1016/j.promfg.2019.02.316
65. Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In 2013 international conference on collaboration technologies and systems (CTS) (pp. 42-47). IEEE.
66. Sahoo, S. (2022). Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management. International Journal of Production Research, 60(22), 6793–6821. https://doi.org/10.1080/00207543.2021.1919333
67. Saura, J. R., Herráez, B. R., & Reyes-Menendez, A. (2019). Comparing a traditional approach for financial Brand Communication Analysis with a Big Data Analytics technique. IEEE Access, 7, 37100-37108.
68. Shi, W., Wang, H., & He, S. (2013). Sentiment analysis of Chinese microblogging based on sentiment ontology: A case study of ‘7.23 Wenzhou Train Collision.’ Connection Science, 25(4), 161–178. https://doi.org/10.1080/09540091.2013.851172
69. Shirdastian, H., Laroche, M., & Richard, M. O. (2019). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48, 291–307. https://doi.org/10.1016/j.ijinfomgt.2017.09.007
70. 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.
71. Smith, A. C. T., Graetz, B. R., & Westerbeek, H. M. (2006). Brand personality in a membership-based organisation. International Journal of Nonprofit and Voluntary Sector Marketing, 11(3), 251–266. https://doi.org/10.1002/nvsm.51
72. Smith, A. C., Stavros, C., & Westberg, K. (2017). Brand fans: Lessons from the World's Greatest Sporting Brands. Springer.
73. Swaminathan, V. (2016). Branding in the digital era: new directions for research on customer-based brand equity. AMS Review, 6(1), 33–38. https://doi.org/10.1007/s13162-016-0077-0
74. Sung, Y., & Kim, J. (2010). Effects of brand personality on brand trust and brand affect. Psychology and Marketing, 27(7), 639–661. https://doi.org/10.1002/mar.20349
75. Tan, K. H. (2018). Managerial perspectives of big data analytics capability towards product innovation. Strategic Direction, 34(8), 33–35. https://doi.org/10.1108/sd-06-2018-0134
76. Vidya, N. A., Fanany, M. I., & Budi, I. (2015). Twitter sentiment to analyze net brand reputation of mobile phone providers. Procedia Computer Science, 72, 519–526. https://doi.org/10.1016/j.procs.2015.12.159
77. Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566. https://doi.org/10.1016/j.jbusres.2015.10.017
78. Xu, Z., Zhu, G., Metawa, N., & Zhou, Q. (2022). Machine learning based customer meta-combination brand equity analysis for marketing behavior evaluation. Information Processing & Management, 59(1), 102800. https://doi.org/10.1016/j.ipm.2021.102800
79. Verma, S. (2022). Sentiment analysis of public services for smart society: Literature review and future research directions. Government Information Quarterly, 39(3), 101708. https://doi.org/10.1016/j.giq.2022.101708
80. Zarezadeh, Z. Z., Rastegar, R., & Xiang, Z. (2022). Big data analytics and hotel guest experience: A critical analysis of the literature. International Journal of Contemporary Hospitality Management, 34(6), 2320–2336. https://doi.org/10.1108/ijchm-10-2021-1293
81. Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied Artificial Intelligence, 17(5–6), 375–381. https://doi.org/10.1080/713827180
82. Zhang, X., & Kim, H. S. (2021). Customer experience and satisfaction of Disneyland Hotel through big data analysis of online customer reviews. Sustainability, 13(22), 12699. https://doi.org/10.3390/su132212699
83. Zhao, Y., Xu, X., & Wang, M. (2019). Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. International Journal of Hospitality Management, 76, 111–121. https://doi.org/10.1016/j.ijhm.2018.03.017
84. Zhao, Z., Tang, M., Tang, W., Wang, C., & Chen, X. (2022). Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis. Neurocomputing, 500, 124–134. https://doi.org/10.1016/j.neucom.2022.05.045
85. Zimbra, D., Ghiassi, M., & Lee, S. (2016, January). Brand-related Twitter sentiment analysis using feature engineering and the dynamic architecture for artificial neural networks. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 1930-1938). IEEE.