Araştırma Makalesi
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PAZARLAMADA KONU MODELLEMESİ: LİTERATÜR TARAMASI VE BİLİMETRİK ANALİZ

Yıl 2023, Cilt: 4 Sayı: 1, 58 - 89, 30.07.2023
https://doi.org/10.54439/gupayad.1316544

Öz

Amaç: Bu çalışma, pazarlama araştırmalarında konu modellemesinin uygulanması üzerine kapsamlı bir literatür incelemesi gerçekleştirirken, alanda ortaya çıkan eğilimleri, hâkim temaları ve potansiyel gelecek yönelimleri belirlemeyi amaçlamaktadır. Gereç ve Yöntem: Çalışmada, bilimsel araştırmaları incelemeye yönelik niceliksel bir yaklaşım olan bilimetrik analiz ve nitel sistematik literatür taraması yöntemleri kullanılmaktadır. Bulgular: Pazarlama alanında önde gelen akademik dergilerden toplanan 54 araştırma makalesinin titizlikle incelenmesi sonucunda, konu modellemenin akademik yazında giderek daha fazla ilgi çektiği ve Gizli Dirichlet Ayrımının (LDA) konu modelleme yaklaşımının pazarlama çalışmalarında en yaygın kullanılan yöntem olduğu ortaya koyulmuştur. Bununla beraber konu modelleme uygulamalarının çoğunlukla başka bir metodoloji ile birleştirilerek kullanıldığı gözlemlenmiştir. Son olarak konu modelleme metodolojilerinin uygulama süreçleri irdelenmiştir. Sonuç: Pazarlama alanındaki literatür taraması, segmentasyon, müşteri davranışları, sosyal medya pazarlaması ve marka yönetimi gibi ana araştırma kümelerini vurgulayarak, konu modellemenin çeşitli araştırma alanlarındaki uygulanabilirliğini göstermiştir.

Kaynakça

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TOPIC MODELING IN MARKETING: LITERATURE REVIEW AND SCIENTOMETRIC ANALYSIS

Yıl 2023, Cilt: 4 Sayı: 1, 58 - 89, 30.07.2023
https://doi.org/10.54439/gupayad.1316544

Öz

Purpose: This study aims to identify emerging trends, dominant topic and potential future directions in the field, while conducting a comprehensive literature review on the application of topic modelling in marketing research. Materials and Methods: The study employs a quantitative approach to analyzing scientific research, the scientometric analysis, and a qualitative systematic literature review. Findings: A meticulous review of 54 research articles collected from leading academic journals in the field of marketing revealed that topic modelling has attracted increasing attention in the academic literature and that the Latent Dirichlet Decomposition (LDA) topic modelling approach is the most widely used method in marketing studies. However, it has been observed that topic modelling applications are mostly used in combination with another methodology. Finally, the application processes of topic modelling methodologies are examined. Conclusion: The literature review in the field of marketing has shown the applicability of topic modelling in various research areas, highlighting the main research clusters such as segmentation, customer behavior, social media marketing and brand management.

Kaynakça

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  • Quan, X., Kit, C., Ge, Y., & Pan, S. J. (2015). Short and sparse text topic modeling via self-aggregation. IJCAI International Joint Conference on Artificial Intelligence, 2015-Janua, 2270-2276.
  • Quezado, T. C. C., Cavalcante, W. Q. F., Fortes, N., & Ramos, R. F. (2022). Corporate social responsibility and marketing: a bibliometric and visualization analysis of the literature between the years 1994 and 2020. Sustainability 2022, Vol. 14, Page 1694, 14(3), 1694. https://doi.org/10.3390/SU14031694
  • Ramage, D., Rosen, E., Chuang, J., Manning, C. D., & McFarland, D. A. (2009, December). Topic modeling for the social sciences. In NIPS 2009 workshop on applications for topic models: text and beyond (Vol. 5, No. 27, pp. 1-4).
  • Reisenbichler, M., & Reutterer, T. (2019). Topic modeling in marketing: recent advances and research opportunities. Journal of Business Economics, 89(3), 327-356. https://doi.org/10.1007/s11573-018-0915-7
  • Rosner, F., Hinneburg, A., Röder, M., Nettling, M., & Both, A. (2014). Evaluating topic coherence measures. https://arxiv.org/abs/1403.6397v1
  • Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. WSDM 2015-Proceedings of the 8th ACM International Conference on Web Search and Data Mining, 399-408. https://doi.org/10.1145/2684822.2685324
  • Schroder, N., Falke, A., Hruschka, H., & Reutterer, T. (2019). Analyzing the Browsing Basket: A latent ınterests-based segmentation tool. Journal of Interactive Marketing, 47, 181-197. https://doi.org/10.1016/j.intmar.2019.05.003
  • Serenko, A. (2013). Meta-analysis of scientometric research of knowledge management: Discovering the identity of the discipline. Journal of Knowledge Management, 17(5), 773-812. https://doi.org/10.1108/JKM-05-2013-0166
  • Shankar, V., & Parsana, S. (2022). An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing. J. Acad. Mark. Sci., 50(6), 1324-1350. https://doi.org/10.1007/s11747-022-00840-3
  • Silwattananusarn, T., & Kulkanjanapiban, P. (2022). A text mining and topic modeling based bibliometric exploration of information science research. IAES International Journal of Artificial Intelligence (IJ-AI), 11(3), 1057-1065. https://doi.org/10.11591/IJAI.V11.I3.PP1057-1065
  • Simons, K. (2008). The misused impact factor. Science, 5899(322), 165-165. https://doi.org/10.1126/science.1165316
  • Swaminathan, V., Schwartz, H. A., Menezes, R., & Hill, S. (2022). The language of brands in social media: Using topic modeling on social media conversations to drive brand strategy. Journal of Interactive Marketing, 57(2), 255-277. https://doi.org/10.1177/10949968221088275
  • Şakar, G. D., & Cerit, A. G. (2013). Uluslararası alan indekslerinde türkiye pazarlama yazını: bibliyometrik analizler ve nitel bir araştırma. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 27(4), 37-62.
  • Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198-215.
  • Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research, 51(4), 463-479. https://doi.org/10.1509/jmr.12.0106
  • Toubia, O. (2021). A poisson factorization topic model for the study of creative documents (and Their Summaries). Journal of Marketing Research, 58(6), 1142-1158. https://doi.org/10.1177/0022243720943209
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  • Wang, G. G., Gilley, J. W., & Sun, J. Y. (2012). The “Science of HRD Research”: reshaping HRD research through scientometrics. Human Resource Development Review, 11(4), 500-520. https://doi.org/10.1177/1534484312452265
  • Wu, L., Dodoo, N. A., Wen, T. J., & Ke, L. (2022). Understanding Twitter conversations about artificial intelligence in advertising based on natural language processing. Int. J. Advert., 41(4), 685-702. https://doi.org/10.1080/02650487.2021.1920218
  • Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013). A biterm topic model for short texts. Proceedings of the 22nd international conference on World Wide Web, 1445-1456. https://doi.org/10.1145/2488388.2488514
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Toplam 115 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Pazarlama Araştırma Metodolojisi
Bölüm Araştırma Makaleleri
Yazarlar

Batuhan Çullu 0000-0003-4969-1466

Gamze Arabelen 0000-0001-5280-7875

Erken Görünüm Tarihi 21 Temmuz 2023
Yayımlanma Tarihi 30 Temmuz 2023
Gönderilme Tarihi 19 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 4 Sayı: 1

Kaynak Göster

APA Çullu, B., & Arabelen, G. (2023). PAZARLAMADA KONU MODELLEMESİ: LİTERATÜR TARAMASI VE BİLİMETRİK ANALİZ. Güncel Pazarlama Yaklaşımları Ve Araştırmaları Dergisi, 4(1), 58-89. https://doi.org/10.54439/gupayad.1316544

Dizinler (Indexing)

31143 21387  3122531320257993114421388  21386  24076 28325 28331 28684