Doğal Afet Sonrası Yorumların Makine Öğrenmesi Yöntemleri ile Sınıflandırılması

Abstract

Sosyal medya kullanımının yaygınlaşması, tüketicilerin markaları değelendirmesine, markalar ve aynı markanın diğer kullanıcılarına doğrudan etkileşimde bulunmasına olanak sağlamaktadır. 6 Şubat 2023 tarihinde meydana gelen depremin ardından Türkiye’nin on bir ilinde iki küresel marka olan Netflix ve Starbucks’a yönelik marka nefreti gözlemlenmiştir. Her iki marka da depremzedelere ve marka elçilerine gerekli hassasiyeti ve empatiyi göstermemekle suçlandı. Bu çalışmanın amacı, denetimli makine öğrenme yöntemlerini kullanarak tüketici tarafından oluşturulan içeriklerdeki marka nefretini incelemek ve sınıflandırmaktadır. Pazarlama disiplininde marka nefretinin yapısı çeşitli veri toplama yöntemleri ile kapsamlı biçimde araştırılmıştır ancak bu çalışma, marka nefretinin makine öğrenmesi yöntemleri ile incelendiği ilk girişimlerden biridir. Klasik polarizasyon işleminin aksine etiketleme işlemi yorumlardaki duygu yüküne bağlı olarak yapılmıştır; 0 nötr tepkileri, -1 negatif duygusal tepkileri ve -2 negatif ilişkisel tepkileri ifade etmektedir. Analiz sonuçlarına göre Destek Vektör Makineleri (DVM) yöntemi bu fenomenin açıklanmasında en başarılı algoritma olarak bulunmuştur.

Keywords

• Duygu analizi
• makine öğrenmesi yöntemleri
• marka nefreti.

Sentiment Classification of Post-Earthquake Consumer Brand Hate on Social Media Using Machine Learning Techniques

Abstract

The widespread use of social media allows consumers to evaluate brands and to get into a direct interaction with brands and other followers of the same brands. After the devastating earthquake on February 6th, 2023, in ten provinces in Turkey a social media brand hatred was observed on two global brands Netflix and Starbucks. Brands were accused of not showing the necessary sensitivity and empathy towards the affected and the brand devotees. The objective of this study is to examine and classify brand hatred in online consumer-generated content using supervised machine learning methods. While the construct of brand hate has been extensively investigated in the discipline of marketing using different data collection methodologies, this is one of the first attempts to use machine learning methods for the analysis of the phenomenon. Unlike classic polarization, the labeling process was associated with the size of brand hatred; 0 denotes neutral reactions, -1 negative emotional reactions, and -2 negative relationship reactions. Support Vector Machines (SVM) was identified as the most successful algorithm for the explanation of the phenomenon.

Keywords

• Sentiment analysis
• machine learning techniques
• brand hate

References

1. Al Amrani, Y., Lazaar, M., & El Kadiri, K. E. (2018). Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis. Procedia Computer Science, 127, 511–520. https://doi.org/10.1016/j.procs.2018.01.150.
2. Alba, J. W., & Lutz, R. J. (2013). Broadening (and narrowing) the scope of brand relationships. Journal of Consumer Psychology, 23(2), 265–268. https://doi.org/10.1016/j.jcps.2013.01.005.
3. Alam, M. T., Sohail, S. S., Ubaid, S., Shakil, Ali, Z., Hijji, M., Saudagar, A. K. J., & Muhammad, K. (2022). It’s Your Turn, Are You Ready to Get Vaccinated? Towards an Exploration of Vaccine Hesitancy Using Sentiment Analysis of Instagram Posts. Mathematics, 10(22), 4165. https://doi.org/10.3390/math10224165.
4. Alzate, M., Arce-Urriza, M., & Cebollada, J. (2022). Mining the text of online consumer reviews to analyze brand image and brand positioning. Journal of Retailing and Consumer Services, 67, 102989. https://doi.org/10.1016/j.jretconser.2022.102989.
5. Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937–1967. https://doi.org/10.1007/s10462-020-09896-5.
6. Brandão, A., & Popoli, P. (2022). “I’m hatin’ it”! Negative consumer–brand relationships in online anti-brand communities. European Journal of Marketing, 56(2), 622–650. https://doi.org/10.1108/EJM-03-2020-0214.
7. Cui, G., Wong, M. L., & Lui, H.-K. (2006). Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming. Management Science, 52(4), 597–612. https://doi.org/10.1287/mnsc.1060.0514.
8. Cui, J., Wang, Z., Ho, S.-B., & Cambria, E. (2023). Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10386-z.

9. Demircan, M., Seller, A., Abut, F., & Akay, M. F. (2021). Developing Turkish sentiment analysis models using machine learning and e-commerce data. International Journal of Cognitive Computing in Engineering, 2, 202–207. https://doi.org/10.1016/j.ijcce.2021.11.003.
10. Fetscherin, M. (2019). The five types of brand hate: How they affect consumer behavior. Journal of Business Research, 101, 116–127. https://doi.org/10.1016/j.jbusres.2019.04.017.
11. Fournier, S. (1998). Consumers and Their Brands: Developing Relationship Theory in Consumer Research. Journal of Consumer Research, 24(4), 343–353. https://doi.org/10.1086/209515.
12. Ghallab, A., Mohsen, A., & Ali, Y. (2020). Arabic Sentiment Analysis: A Systematic Literature Review. Applied Computational Intelligence and Soft Computing, 2020, 1–21. https://doi.org/10.1155/2020/7403128.
13. Krishnamurthy, S., & Kucuk, S. U. (2009). Anti-branding on the internet. Journal of Business Research, 62(11), 1119-1126. https://doi.org/10.1016/j.jbusres.2008.09.003.
14. Kim, T., Jo, H., Yhee, Y., & Koo, C. (2022). Robots, artificial intelligence, and service automation (RAISA) in hospitality: sentiment analysis of YouTube streaming data. Electronic Markets, 32(1), 259–275. https://doi.org/10.1007/s12525-021-00514-y.
15. Kucuk, S. U. (2008). Negative Double Jeopardy: The role of anti-brand sites on the internet. Journal of Brand Management, 15(3), 209–222. https://doi.org/10.1057/palgrave.bm.2550100.
16. Kucuk, S. U. (2010). Negative Double Jeopardy revisited: A longitudinal analysis. Journal of Brand Management, 18(2), 150–158. https://doi.org/10.1057/bm.2010.27.
17. Kucuk, S. U. (2018). Macro-level antecedents of consumer brand hate. Journal of Consumer Marketing, 35(5), 555–564. https://doi.org/10.1108/JCM-10-2017-2389.
18. Kucuk, S. U. (2019). Consumer Brand Hate: Steam rolling whatever I see. Psychology & Marketing, 36(5), 431–443. https://doi.org/10.1002/mar.21175.
19. Kucuk, S. U. (2019). Consumer Brand Hate: Steam rolling whatever I see. Psychology & Marketing, 36(5), 431–443. https://doi.org/10.1002/mar.21175.
20. Kucuk, S. U. (2021). Developing a theory of brand hate: Where are we now? Strategic Change, 30(1), 29–33. https://doi.org/10.1002/jsc.2385.
21. Lee, E., Rustam, F., Ashraf, I., Washington, P. B., Narra, M., & Shafique, R. (2022). Inquest of Current Situation in Afghanistan Under Taliban Rule Using Sentiment Analysis and Volume Analysis. IEEE Access, 10, 10333–10348. https://doi.org/10.1109/ACCESS.2022.3144659.
22. Li, S., Xie, Z., Chiu, D. K. W., & Ho, K. K. W. (2023). Sentiment Analysis and Topic Modeling Regarding Online Classes on the Reddit Platform: Educators versus Learners. Applied Sciences, 13(4), 2250. https://doi.org/10.3390/app13042250.
23. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005.
24. Mitchell, T. (1997). Machine learning. McGraw Hill.
25. Ngai, E. W. T., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35–48. https://doi.org/10.1016/j.jbusres.2022.02.049.
26. Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31, 527–541. https://doi.org/10.1016/j.chb.2013.05.024.
27. Omeraki, Çekirdekci, Ş. & Erarslan, E. (2023) National hate towards global brand. Akademik Hassasiyetler, 10(22), 335-356. https://doi.org/10.58884/akademik-hassasiyetler.1327729
28. Osorio Angel, S., Peña Pérez Negrón, A., & Espinoza-Valdez, A. (2021). Systematic literature review of sentiment analysis in the Spanish language. Data Technologies and Applications, 55(4), 461–479. https://doi.org/10.1108/DTA-09-2020-0200.
29. Ounacer, S., Mhamdi, D., Ardchir, S., Daif, A., & Azzouazi, M. (2023). Customer Sentiment Analysis in Hotel Reviews Through Natural Language Processing Techniques. International Journal of Advanced Computer Science and Applications, 14(1). https://doi.org/10.14569/IJACSA.2023.0140162.
30. Pratama, A. R. (2022). Sentiment Analysis of Facebook Posts through Special Reactions: The Case of Learning from Home in Indonesia Amid COVID-19. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 8(1), 83. https://doi.org/10.26555/jiteki.v8i1.23615.
31. Saigal, P., & Khanna, V. (2020). Multi-category news classification using Support Vector Machine based classifiers. SN Applied Sciences, 2(3), 458. https://doi.org/10.1007/s42452-020-2266-6.
32. Sailunaz, K., & Alhajj, R. (2019). Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36, 101003. https://doi.org/10.1016/j.jocs.2019.05.009.
33. Sánchez-Rada, J. F., & Iglesias, C. A. (2019). Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion, 52, 344–356. https://doi.org/10.1016/j.inffus.2019.05.003.
34. Schapire, R. E., & Singer, Y. (1997). Using output codes to boost multiclass learning problems. In Machine Learning: Proceedings of the Fourteenth International Conference, 313–321.
35. Sohaib, M., & Han, H. (2023). Building value co-creation with social media marketing, brand trust, and brand loyalty. Journal of Retailing and Consumer Services, 74, 103442.
36. Singh, R., & Singh, R. (2023). Applications of sentiment analysis and machine learning techniques in disease outbreak prediction – A review. Materials Today: Proceedings, 81, 1006–1011. https://doi.org/10.1016/j.matpr.2021.04.356.
37. Taherdoost, H., & Madanchian, M. (2023). Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research. Computers, 12(2), 37. https://doi.org/10.3390/computers12020037.
38. Tan, K. L., Lee, C. P., & Lim, K. M. (2023). A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Applied Sciences, 13(7), 4550. https://doi.org/10.3390/app13074550.
39. Tekinbaş Özkaya, F., Durak, M. G., Doğan, O., Bulut, Z. A., & Haas, R. (2021). Sustainable Consumption of Food: Framing the Concept through Turkish Expert Opinions. Sustainability, 13(7), 3946. https://doi.org/10.3390/su13073946.
40. Vapnik, V. N. (2000). The Nature of Statistical Learning Theory. Springer New York. https://doi.org/10.1007/978-1-4757-3264-1.
41. Wang, Y., & Chen, Y. (2023). Characterizing discourses about COVID-19 vaccines on Twitter: a topic modeling and sentiment analysis approach. Journal of Communication in Healthcare, 16(1), 103–112. https://doi.org/10.1080/17538068.2022.2054196.
42. Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1.
43. Zarantonello, L., Romani, S., Grappi, S., & Fetscherin, M. (2018). Trajectories of brand hate. Journal of Brand Management, 25(6), 549–560. https://doi.org/10.1057/s41262-018-0105-5.
44. Zhang, C., & Laroche, M. (2020). Brand hate: a multidimensional construct. Journal of Product & Brand Management, 30(3), 392–414. https://doi.org/10.1108/JPBM-11-2018-2103.
45. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. WIREs Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1253.