Araştırma Makalesi
BibTex RIS Kaynak Göster

PAZARLAMADA KONU MODELLEMESİ: LİTERATÜR TARAMASI VE BİLİMETRİK ANALİZ

Yıl 2023, , 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

  • Alantari, H. J., Currim, I. S., Deng, Y., & Singh, S. (2022). An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. Int. J. Res. Mark., 39(1), 1-19. https://doi.org/10.1016/j.ijresmar.2021.10.011
  • Aleem, M., Sufyan, M., Ameer, I., & Mustak, M. (2023). Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda. Journal od Business Research, 154. https://doi.org/10.1016/j.jbusres.2022.113303
  • Aletras, N., & Stevenson, M. (2013). Evaluating topic coherence using distributional semantics. Proceedings of the 10th International Conference on Computational Semantics, IWCS 2013-Long Papers.
  • Aliyev, F., Urkmez, T., & Wagner, R. (2019). A comprehensive look at luxury brand marketing research from 2000 to 2016: a bibliometric study and content analysis. Management Review Quarterly, 69, 233-264.
  • Arslan, E., (2022). Nitel araştırmalarda geçerlilik ve güvenilirlik. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 51(Özel Sayı 1), 395-407. https://10.30794/pausbed.1116878
  • Arunachalam, S., Bahadir, S. C., Bharadwaj, S. G., ve Guesalaga, R. (2020). New product introductions for low-income consumers in emerging markets. J. Acad. Mark. Sci., 48(5), 914-940. https://doi.org/10.1007/s11747-019-00648-8
  • Avasthi, S., Chauhan, R., & Acharjya, D. P. (2022). Topic modeling techniques for text mining over a large-scale scientific and biomedical text corpus. International Journal of Ambient Computing and Intelligence, 13(1), 1-18. https://doi.org/10.4018/IJACI.293137
  • Avey, M., Moher, D., Sullivan, K., Fergusson, D., Griffin, G., Grimshaw, J., … & McIntyre, L. (2016). The devil is in the details: incomplete reporting in preclinical animal research. PLoS ONE, 11(11), e0166733. https://doi.org/10.1371/journal.pone.0166733
  • Aylan, F. K., Başoda, A. (2022). Scopus veri tabanı üzerinden etkinlik pazarlaması alanına ilişkin panoramik bir bakış, İşletme Araştırmaları Dergisi, 14(3), 1841-1858.
  • Bennett, R., & Vijaygopal, R. (2019). What if the company’s “charity of the year” is an organisation that deals with severe to moderate mental disability? A case study of fundraising problems and possibilities. Journal of Social Marketing, 9(2), 161-179. https://doi.org/10.1108/JSOCM-01-2019-0004
  • Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020). Uniting the tribes: Using text for marketing İnsight. Journal of Marketing, 84(1), 1-25. https://doi.org/10.1177/0022242919873106
  • Berger, J., Packard, G., Boghrati, R., Hsu, M., Humphreys, A., Luangrath, A., Moore, S., Nave, G., Olivola, C., & Rocklage, M. (2022). Wisdom from words: marketing insights from text. Marketing Letters, 33(3), 365-377. https://doi.org/10.1007/s11002-022-09635-6
  • Bernardi, C. L., & Alhamdan, N. (2022). Social media analytics for nonprofit marketing: #Downsyndrome on Twitter and Instagram. Journal of Philanthropy and Marketing, 27(4). https://doi.org/10.1002/nvsm.1739
  • Blanchard, S. J., Aloise, D., & Desarbo, W. S. (2017). Extracting summary piles from sorting task data. J. Mark. Res., 54(3), 398-414. https://doi.org/10.1509/jmr.15.0388
  • Blasco-Arcas, L., Lee, H.-H. M., Kastanakis, M. N., Alcañiz, M., & Reyes-Menendez, A. (2022). The role of consumer data in marketing: A research agenda. J. Bus. Res., 146, 436-452. https://doi.org/10.1016/j.jbusres.2022.03.054
  • Blei, D. M. (2012). Surveying a suite of algorithms that offer a solution to managing large document archives. Probabilistic topic models. Communications of the ACM, 55(4), 77-84. ttps://doi.org/10.1145/2133806.2133826
  • Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of Science. https://doi.org/10.1214/07-AOAS114, 1(1), 17-35. https://doi.org/10.1214/07-AOAS114
  • Blei, D. M., & Mcauliffe, J. D. (2007). Supervised topic models. Advances in Neural Information Processing Systems, 20. www.digg.com
  • Blei, D. M., & Ng, A. Y. (2003). Latent dirichlet allocation. Journal of Machine Learning, 3, 993-1022. https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=https://githubhelp.com
  • Borah, A., & Skiera, B. (2021). Marketing and investor behavior: Insights, introspections, and indications. Int. J. Res. Mark., 38(4), 811-816. https://doi.org/10.1016/j.ijresmar.2021.09.011
  • Büschken, J., & Allenby, G. M. (2016). Sentence-Based text analysis for customer reviews. Marketing Science, 35(6), 831-998. https://doi.org/10.1287/mksc.2016.0993, 35(6), 953-975.
  • CABS, (2021). Academic Journal Guide. https://charteredabs.org/academic-journal-guide-2021-view/ Cavalcante, W. Q. de F., Coelho, A., & Bairrada, C. M. (2021). Sustainability and tourism marketing: A bibliometric analysis of publications between 1997 and 2020 using vosviewer software. Sustainability, 13(9), 4987. https://doi.org/10.3390/SU13094987
  • Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems 22-Proceedings of the 2009 Conference, 288-296. http://rexa.info
  • Cho, Y. J., Fu, P. W., & Wu, C. C. (2017). Popular research topics in marketing journals, 1995–2014. J. Interact. Mark., 40(1), 52-72. https://doi.org/10.1016/j.intmar.2017.06.003
  • Churchill, R., ve Singh, L. (2022). The evolution of topic modeling. ACM Computing Surveys, 54(10). https://doi.org/10.1145/3507900
  • Culasso, F., Gavurova, B., Crocco, E., & Giacosa, E. (2023). Empirical identification of the chief digital officer role: A latent Dirichlet allocation approach. Journal of Business Research, 154. https://doi.org/10.1016/j.jbusres.2022.113301
  • Das, K., Patel, J. D., Sharma, A., & Shukla, Y. (2023). Creativity in marketing: Examining the intellectual structure using scientometric analysis and topic modeling. Journal of Business Research, 154. https://doi.org/10.1016/j.jbusres.2022.113384
  • Crossley, S. A., Dascalu, M., & McNamara, D. S. (2017). How important is size? An investigation of corpus size and meaning in both Latent Semantic Analysis and Latent Dirichlet Allocation. Flairs 2017-Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference, 293-296. http://lsa.colorado.edu/spaces.html
  • Daud, A., Li, J., Zhou, L., & Muhammad, F. (2010). Knowledge discovery through directed probabilistic topic models: A survey. Frontiers of Computer Science in China, 4(2), 280-301. https://doi.org/10.1007/S11704-009-0062-Y/METRICS
  • Denny, M., & Spirling, A. (2017). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 26(2), 168-189. https://doi.org/10.1017/pan.2017.44
  • Dew, R., Ansari, A., & Li, Y. (2020). Modeling dynamic heterogeneity using gaussian processes. Journal of Marketing Research, 57(1), 55-77. https://doi.org/10.1177/0022243719874047
  • Dzyabura, D., & Peres, R. (2021). Visual elicitation of brand perception. Journal of Marketing, 85(4), 44-66. https://doi.org/10.1177/0022242921996661
  • Fresneda, J. E., Burnham, T. A., & Hill, C. H. (2021). Structural topic modelling segmentation: a segmentation method combining latent content and customer context. Journal of Marketing Management, 37(7-8), 792-812. https://doi.org/10.1080/0267257X.2021.1880464
  • Gao, S., Hu, Y., Janowicz, K., & McKenzie, G. (2013). A spatiotemporal scientometrics framework for exploring the citation impact of publications and scientists. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 204-213. https://doi.org/10.1145/2525314.2525368
  • Garner, B., Thornton, C., Luo Pawluk, A., Mora Cortez, R., Johnston, W., & Ayala, C. (2022). Utilizing text-mining to explore consumer happiness within tourism destinations. J. Bus. Res., 139, 1366-1377. https://doi.org/10.1016/j.jbusres.2021.08.025
  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(SUPPL. 1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  • Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292-1307. https://doi.org/10.1016/J.IPM.2018.05.006
  • Han, S., Han, J. K., Im, I., Jung, S. I., & Lee, J. W. (2022). Mapping consumer’s cross-device usage for online search: Mobile- vs. PC-based search in the purchase decision process. J. Bus. Res., 142, 387-399. https://doi.org/10.1016/j.jbusres.2021.12.051
  • Hannigan, T. R., Haans, R. F., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M. S., ... & Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586-632. https://doi.org/10.5465/annals.2017.0099
  • Harzing, A. W. (2022). Harzing’s journal quality list. https://harzing.com/download/jql69_subject_2.pdf Humphreys, A., & Wang, R. J. H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44(6), 1274-1306. https://doi.org/10.1093/JCR/UCX104
  • Jacobs, B., Fok, D., & Donkers, B. (2021). Understanding large-scale dynamic purchase behavior. Mark. Sci., 40(5), 844-870. https://doi.org/10.1287/mksc.2020.1279
  • Jacobs, B. J. D., Donkers, B., & Fok, D. (2016). Model-Based purchase predictions for large assortments. Marketing Science, 35(3), 389-404. https://doi.org/10.1287/mksc.2016.0985
  • Jedidi, K., Schmitt, B. H., Ben Sliman, M., & Li, Y. (2021). R2M index 1.0: Assessing the practical relevance of academic marketing articles. J. Mark., 85(5), 22-41. https://doi.org/10.1177/00222429211028145
  • Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi.org/10.1007/S11042-018-6894-4/TABLES/11
  • Kavak, B., & Sunaoǧlu, Ş. K. (2020). Pazarlama bilim dalında yazılmış yüksek lisans ve doktora tezlerinin bibliyometrik profilinin incelenmesi. Third Sector Social Economic Review, 55(4), 2997-3021. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.20.12.1509
  • Kherwa, P., & Bansal, P. (2020). Topic modeling: A comprehensive review. EAI Endorsed Transactions on Scalable Information Systems, 7(24), 1-16. https://doi.org/10.4108/eai.13-7-2018.159623
  • Kim, D. Y., & Kim, S. Y. (2022). The impact of customer-generated evaluation information on sales in online platform-based markets. J. Retail. Consum. Serv., 68(103016), 103016. https://doi.org/10.1016/j.jretconser.2022.103016
  • Kim, M. C., Zhu, Y., Kim, M. C., & Zhu, Y. (2018). Scientometrics of Scientometrics: Mapping historical footprint and emerging technologies in scientometrics. Scientometrics. https://doi.org/10.5772/INTECHOPEN.77951
  • Kolomoyets, Y., & Dickinger, A. (2023). Understanding value perceptions and propositions: A machine learning approach. J. Bus. Res., 154(113355), 113355. https://doi.org/10.1016/j.jbusres.2022.113355
  • Kumar, P. (2022). Managing service flexibility in healthcare for improved customer experience: a data-driven approach. J. Strat. Mark., 1-22. https://doi.org/10.1080/0965254x.2022.2096671
  • Lee, L. W., Dabirian, A., McCarthy, I. P., & Kietzmann, J. (2020). Making sense of text: artificial intelligence-enabled content analysis. Eur. J. Mark., 54(3), 615-644. https://doi.org/10.1108/ejm-02-2019-0219
  • Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881-894. https://doi.org/10.1509/JMKR.48.5.881
  • Lei, S., & Zhang, Y. (2020). The role of the media in socially responsible investing. Int. J. Bank Mark., 38(4), 823-841. https://doi.org/10.1108/ijbm-09-2019-0332
  • Letunovska, N., Lyuolyov, O., Pimonenko, T., & Aleksandrov, V. (2021). Environmental management and social marketing: a bibliometric analysis. E3S Web of Conferences, 234, 00008. https://doi.org/10.1051/E3SCONF/202123400008
  • Li, H., & Ma, L. (2020). Charting the path to purchase using topic models. Journal of Marketing Research, 57(6), 1019-1036. https://doi.org/10.1177/0022243720954376
  • Li, M., Zhao, L., & Srinivas, S. (2023). It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers. Int. J. Consum. Stud. https://doi.org/10.1111/ijcs.12895
  • Lim, W. M., Gupta, G., Biswas, B., & Gupta, R. (2022). Collaborative consumption continuance: a mixed-methods analysis of the service quality-loyalty relationship in ride-sharing services. Electron. Mark., 32(3), 1463-1484. https://doi.org/10.1007/s12525-021-00486-z
  • Liu, L., Tang, L., Dong, W., Yao, S., & Zhou, W. (2016). An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1). https://doi.org/10.1186/S40064-016-3252-8
  • Loureiro, S. M. C., Guerreiro, J., Eloy, S., Langaro, D., & Panchapakesan, P. (2019). Understanding the use of virtual reality in marketing: A text mining-based review. J. Bus. Res., 100, 514-530. https://doi.org/10.1016/j.jbusres.2018.10.055
  • Marshall, P. (2022). A latent allocation model for brand awareness and mindset metrics. Int. J. Mark. Res., 64(4), 526-540. https://doi.org/10.1177/14707853211040052
  • Mathaisel, D. F. X., & Comm, C. L. (2021). Political marketing with data analytics. J. Mark. Anal., 9(1), 56-64. https://doi.org/10.1057/s41270-020-00097-1
  • Meena, P., & Kumar, G. (2022). Online food delivery companies’ performance and consumers expectations during Covid-19: An investigation using machine learning approach. J. Retail. Consum. Serv., 68(103052), 103052. https://doi.org/10.1016/j.jretconser.2022.103052
  • Mifrah, S., & Benlahmar, E. H. (2022). Topic modeling with transformers for sentence-Level using coronavirus corpus. International Journal of Interactive Mobile Technologies (IJIM), 16(17), 50-59. https://doi.org/10.3991/IJIM.V16I17.33281
  • Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. EMNLP 2011-Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 262-272.
  • Mingers, J., Macri, F., & Petrovici, D. A. (2012). Using the h-index to measure the quality of journals in the field of business and management. Information Processing & Management, 2(48), 234-241. https://doi.org/10.1016/j.ipm.2011.03.009
  • Mishra, M. (2022). Customer experience: Extracting topics from tweets. Int. J. Mark. Res., 64(3), 334-353. https://doi.org/10.1177/14707853211047515
  • Mistry, D. A., & Shah, A. (2018). Topic detection in Twitter with data mining. SJ Impact Factor: 6, 887. https://doi.org/10.22214/ijraset.2018.4482
  • Moro, S., Lopes, R. J., Esmerado, J., & Botelho, M. (2020). Service quality in airport hotel chains through the lens of online reviewers. J. Retail. Consum. Serv., 56(102193), 102193. https://doi.org/10.1016/j.jretconser.2020.102193
  • Moro, S., Pires, G., Rita, P., & Cortez, P. (2019). A text mining and topic modelling perspective of ethnic marketing research. Journal of Business Research, 103, 275-285. https://doi.org/10.1016/j.jbusres.2019.01.053
  • Moro, S., Pires, G., Rita, P., & Cortez, P. (2020). A cross-cultural case study of consumers’ communications about a new technological product. J. Bus. Res., 121, 438-447. https://doi.org/10.1016/j.jbusres.2018.08.009
  • Mostafa, M. M. (2019). Clustering halal food consumers: A Twitter sentiment analysis. Int. J. Mark. Res., 61(3), 320-337. https://doi.org/10.1177/1470785318771451
  • Mostafa, M. M. (2021). Information diffusion in halal food social media: A social network approach. J. Int. Consum. Mark., 33(4), 471-491. https://doi.org/10.1080/08961530.2020.1818158
  • Mukherjee, P., Dutta, S., & De Bruyn, A. (2022). Did clickbait crack the code on virality? J. Acad. Mark. Sci., 50(3), 482-502. https://doi.org/10.1007/s11747-021-00830-x
  • Muñoz-Leiva, F., Rodríguez López, M. E., Liebana-Cabanillas, F., & Moro, S. (2021). Past, present, and future research on self-service merchandising: a co-word and text mining approach. Eur. J. Mark., 55(8), 2269-2307. https://doi.org/10.1108/ejm-02-2019-0179
  • Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389-404. https://doi.org/10.1016/J.JBUSRES.2020.10.044
  • Nam, H., Joshi, Y. V., & Kannan, P. K. (2017). Harvesting brand information from social tags. J. Mark., 81(4), 88-108. https://doi.org/10.1509/jm.16.0044
  • Netzer, O., Feldman, R., Goldenberg, J., ve Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543. https://doi.org/10.1
  • Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010). Automatic evaluation of topic coherence. NAACL HLT 2010-Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference, 100-108.
  • Nguyen, D. Q., Billingsley, R., Du, L., & Johnson, M. (2015). Improving topic models with latent feature word representations. Transactions of the Association for Computational Linguistics, 3, 299-313. https://doi.org/10.1162/tacl_a_00140
  • Nikolenko, S. I., Koltcov, S., & Koltsova, O. (2017). Topic modelling for qualitative studies. Article Journal of Information Science, 43(1), 88-102. https://doi.org/10.1177/0165551515617393
  • Pahrudin, P., Liu, L. W., & Li, S. Y. (2022). What is the role of tourism management and marketing toward sustainable tourism? A bibliometric analysis approach. Sustainability 2022, Vol. 14, Page 4226, 14(7), 4226. https://doi.org/10.3390/SU14074226
  • Pal, R., Sekh, A. A., Dogra, D. P., Kar, S., Roy, P. P., & Prasad, D. K. (2021). Topic-based video analysis. ACM Computing Surveys (CSUR), 54(6). https://doi.org/10.1145/3459089
  • Pardo, C., Pagani, M., & Savinien, J. (2022). The strategic role of social media in business-to-business contexts. Ind. Mark. Manag., 101, 82-97. https://doi.org/10.1016/j.indmarman.2021.11.010
  • Park, J., Yang, D., & Kim, H. Y. (2023). Text mining-based four-step framework for smart speaker product improvement and sales planning. J. Retail. Consum. Serv., 71(103186), 103186. https://doi.org/10.1016/j.jretconser.2022.103186
  • Patrick, Z., & Hee, O. C. (2020). A bibliometric analysis of global online marketing research trends. International Journal of Academic Research in Business and Social Sciences, 10(5). https://doi.org/10.6007/IJARBSS/V10-I5/7248
  • Poushneh, A., & Rajabi, R. (2022). Can reviews predict reviewers’ numerical ratings? The underlying mechanisms of customers’ decisions to rate products using Latent Dirichlet Allocation (LDA). Journal Of Consumer Marketing, 39(2), 230-241. https://doi.org/10.1108/JCM-09-2020-4114
  • Puranam, D., Narayan, V., & Kadiyali, V. (2017). The effect of calorie posting regulation on consumer opinion: A flexible latent dirichlet allocation model with informative priors. https://doi.org/10.1287/mksc.2017.1048, 36(5), 726-746. https://doi.org/10.1287/MKSC.2017.1048
  • 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
  • Trusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Mark. Sci., 35(3), 405-426. https://doi.org/10.1287/mksc.2015.0956
  • Vallurupalli, V., & Bose, I. (2020). Exploring thematic composition of online reviews: A topic modeling approach. Electronıc Markets, 30(4), 791-804. https://doi.org/10.1007/s12525-020-00397-5
  • Vanhala, M., Lu, C., Peltonen, J., Sundqvist, S., Nummenmaa, J., & Järvelin, K. (2020). The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research. Journal of Business Research, 106, 46-59. https://doi.org/10.1016/J.JBUSRES.2019.09.009
  • Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/J.IS.2020.101582
  • Wallach, H. M., Mimno, D., & McCallum, A. (2009). Rethinking LDA: Why priors matter. Advances in Neural Information Processing Systems 22-Proceedings of the 2009 Conference, 1973-1981. http://rexa.info/
  • 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
  • Ye, F., Xia, Q., Zhang, M., Zhan, Y., & Li, Y. (2022). Harvesting online reviews to identify the competitor set in a service business: Evidence from the hotel industry. J. Serv. Res., 25(2), 301-327. https://doi.org/10.1177/1094670520975143
  • Yi, F., Jiang, B., ve Wu, J. (2020). Topic modeling for short texts via word embedding and document correlation. IEEE Access, 8, 30692-30705. https://doi.org/10.1109/ACCESS.2020.2973207
  • Zhang, J. (2019). What’s yours is mine: exploring customer voice on Airbnb using text-mining approaches. Journal of Consumer Marketing, 36(5), 655-665. https://doi.org/10.1108/JCM-02-2018-2581
  • Zuo, Y., Wu, J., Zhang, H., Lin, H., Wang, F., Xu, K., & Xiong, H. (2016). Topic modeling of short texts. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2105-2114. https://doi.org/10.1145/2939672.2939880

TOPIC MODELING IN MARKETING: LITERATURE REVIEW AND SCIENTOMETRIC ANALYSIS

Yıl 2023, , 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

  • Alantari, H. J., Currim, I. S., Deng, Y., & Singh, S. (2022). An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. Int. J. Res. Mark., 39(1), 1-19. https://doi.org/10.1016/j.ijresmar.2021.10.011
  • Aleem, M., Sufyan, M., Ameer, I., & Mustak, M. (2023). Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda. Journal od Business Research, 154. https://doi.org/10.1016/j.jbusres.2022.113303
  • Aletras, N., & Stevenson, M. (2013). Evaluating topic coherence using distributional semantics. Proceedings of the 10th International Conference on Computational Semantics, IWCS 2013-Long Papers.
  • Aliyev, F., Urkmez, T., & Wagner, R. (2019). A comprehensive look at luxury brand marketing research from 2000 to 2016: a bibliometric study and content analysis. Management Review Quarterly, 69, 233-264.
  • Arslan, E., (2022). Nitel araştırmalarda geçerlilik ve güvenilirlik. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 51(Özel Sayı 1), 395-407. https://10.30794/pausbed.1116878
  • Arunachalam, S., Bahadir, S. C., Bharadwaj, S. G., ve Guesalaga, R. (2020). New product introductions for low-income consumers in emerging markets. J. Acad. Mark. Sci., 48(5), 914-940. https://doi.org/10.1007/s11747-019-00648-8
  • Avasthi, S., Chauhan, R., & Acharjya, D. P. (2022). Topic modeling techniques for text mining over a large-scale scientific and biomedical text corpus. International Journal of Ambient Computing and Intelligence, 13(1), 1-18. https://doi.org/10.4018/IJACI.293137
  • Avey, M., Moher, D., Sullivan, K., Fergusson, D., Griffin, G., Grimshaw, J., … & McIntyre, L. (2016). The devil is in the details: incomplete reporting in preclinical animal research. PLoS ONE, 11(11), e0166733. https://doi.org/10.1371/journal.pone.0166733
  • Aylan, F. K., Başoda, A. (2022). Scopus veri tabanı üzerinden etkinlik pazarlaması alanına ilişkin panoramik bir bakış, İşletme Araştırmaları Dergisi, 14(3), 1841-1858.
  • Bennett, R., & Vijaygopal, R. (2019). What if the company’s “charity of the year” is an organisation that deals with severe to moderate mental disability? A case study of fundraising problems and possibilities. Journal of Social Marketing, 9(2), 161-179. https://doi.org/10.1108/JSOCM-01-2019-0004
  • Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020). Uniting the tribes: Using text for marketing İnsight. Journal of Marketing, 84(1), 1-25. https://doi.org/10.1177/0022242919873106
  • Berger, J., Packard, G., Boghrati, R., Hsu, M., Humphreys, A., Luangrath, A., Moore, S., Nave, G., Olivola, C., & Rocklage, M. (2022). Wisdom from words: marketing insights from text. Marketing Letters, 33(3), 365-377. https://doi.org/10.1007/s11002-022-09635-6
  • Bernardi, C. L., & Alhamdan, N. (2022). Social media analytics for nonprofit marketing: #Downsyndrome on Twitter and Instagram. Journal of Philanthropy and Marketing, 27(4). https://doi.org/10.1002/nvsm.1739
  • Blanchard, S. J., Aloise, D., & Desarbo, W. S. (2017). Extracting summary piles from sorting task data. J. Mark. Res., 54(3), 398-414. https://doi.org/10.1509/jmr.15.0388
  • Blasco-Arcas, L., Lee, H.-H. M., Kastanakis, M. N., Alcañiz, M., & Reyes-Menendez, A. (2022). The role of consumer data in marketing: A research agenda. J. Bus. Res., 146, 436-452. https://doi.org/10.1016/j.jbusres.2022.03.054
  • Blei, D. M. (2012). Surveying a suite of algorithms that offer a solution to managing large document archives. Probabilistic topic models. Communications of the ACM, 55(4), 77-84. ttps://doi.org/10.1145/2133806.2133826
  • Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of Science. https://doi.org/10.1214/07-AOAS114, 1(1), 17-35. https://doi.org/10.1214/07-AOAS114
  • Blei, D. M., & Mcauliffe, J. D. (2007). Supervised topic models. Advances in Neural Information Processing Systems, 20. www.digg.com
  • Blei, D. M., & Ng, A. Y. (2003). Latent dirichlet allocation. Journal of Machine Learning, 3, 993-1022. https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=https://githubhelp.com
  • Borah, A., & Skiera, B. (2021). Marketing and investor behavior: Insights, introspections, and indications. Int. J. Res. Mark., 38(4), 811-816. https://doi.org/10.1016/j.ijresmar.2021.09.011
  • Büschken, J., & Allenby, G. M. (2016). Sentence-Based text analysis for customer reviews. Marketing Science, 35(6), 831-998. https://doi.org/10.1287/mksc.2016.0993, 35(6), 953-975.
  • CABS, (2021). Academic Journal Guide. https://charteredabs.org/academic-journal-guide-2021-view/ Cavalcante, W. Q. de F., Coelho, A., & Bairrada, C. M. (2021). Sustainability and tourism marketing: A bibliometric analysis of publications between 1997 and 2020 using vosviewer software. Sustainability, 13(9), 4987. https://doi.org/10.3390/SU13094987
  • Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems 22-Proceedings of the 2009 Conference, 288-296. http://rexa.info
  • Cho, Y. J., Fu, P. W., & Wu, C. C. (2017). Popular research topics in marketing journals, 1995–2014. J. Interact. Mark., 40(1), 52-72. https://doi.org/10.1016/j.intmar.2017.06.003
  • Churchill, R., ve Singh, L. (2022). The evolution of topic modeling. ACM Computing Surveys, 54(10). https://doi.org/10.1145/3507900
  • Culasso, F., Gavurova, B., Crocco, E., & Giacosa, E. (2023). Empirical identification of the chief digital officer role: A latent Dirichlet allocation approach. Journal of Business Research, 154. https://doi.org/10.1016/j.jbusres.2022.113301
  • Das, K., Patel, J. D., Sharma, A., & Shukla, Y. (2023). Creativity in marketing: Examining the intellectual structure using scientometric analysis and topic modeling. Journal of Business Research, 154. https://doi.org/10.1016/j.jbusres.2022.113384
  • Crossley, S. A., Dascalu, M., & McNamara, D. S. (2017). How important is size? An investigation of corpus size and meaning in both Latent Semantic Analysis and Latent Dirichlet Allocation. Flairs 2017-Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference, 293-296. http://lsa.colorado.edu/spaces.html
  • Daud, A., Li, J., Zhou, L., & Muhammad, F. (2010). Knowledge discovery through directed probabilistic topic models: A survey. Frontiers of Computer Science in China, 4(2), 280-301. https://doi.org/10.1007/S11704-009-0062-Y/METRICS
  • Denny, M., & Spirling, A. (2017). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 26(2), 168-189. https://doi.org/10.1017/pan.2017.44
  • Dew, R., Ansari, A., & Li, Y. (2020). Modeling dynamic heterogeneity using gaussian processes. Journal of Marketing Research, 57(1), 55-77. https://doi.org/10.1177/0022243719874047
  • Dzyabura, D., & Peres, R. (2021). Visual elicitation of brand perception. Journal of Marketing, 85(4), 44-66. https://doi.org/10.1177/0022242921996661
  • Fresneda, J. E., Burnham, T. A., & Hill, C. H. (2021). Structural topic modelling segmentation: a segmentation method combining latent content and customer context. Journal of Marketing Management, 37(7-8), 792-812. https://doi.org/10.1080/0267257X.2021.1880464
  • Gao, S., Hu, Y., Janowicz, K., & McKenzie, G. (2013). A spatiotemporal scientometrics framework for exploring the citation impact of publications and scientists. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 204-213. https://doi.org/10.1145/2525314.2525368
  • Garner, B., Thornton, C., Luo Pawluk, A., Mora Cortez, R., Johnston, W., & Ayala, C. (2022). Utilizing text-mining to explore consumer happiness within tourism destinations. J. Bus. Res., 139, 1366-1377. https://doi.org/10.1016/j.jbusres.2021.08.025
  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(SUPPL. 1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  • Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292-1307. https://doi.org/10.1016/J.IPM.2018.05.006
  • Han, S., Han, J. K., Im, I., Jung, S. I., & Lee, J. W. (2022). Mapping consumer’s cross-device usage for online search: Mobile- vs. PC-based search in the purchase decision process. J. Bus. Res., 142, 387-399. https://doi.org/10.1016/j.jbusres.2021.12.051
  • Hannigan, T. R., Haans, R. F., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M. S., ... & Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586-632. https://doi.org/10.5465/annals.2017.0099
  • Harzing, A. W. (2022). Harzing’s journal quality list. https://harzing.com/download/jql69_subject_2.pdf Humphreys, A., & Wang, R. J. H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44(6), 1274-1306. https://doi.org/10.1093/JCR/UCX104
  • Jacobs, B., Fok, D., & Donkers, B. (2021). Understanding large-scale dynamic purchase behavior. Mark. Sci., 40(5), 844-870. https://doi.org/10.1287/mksc.2020.1279
  • Jacobs, B. J. D., Donkers, B., & Fok, D. (2016). Model-Based purchase predictions for large assortments. Marketing Science, 35(3), 389-404. https://doi.org/10.1287/mksc.2016.0985
  • Jedidi, K., Schmitt, B. H., Ben Sliman, M., & Li, Y. (2021). R2M index 1.0: Assessing the practical relevance of academic marketing articles. J. Mark., 85(5), 22-41. https://doi.org/10.1177/00222429211028145
  • Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi.org/10.1007/S11042-018-6894-4/TABLES/11
  • Kavak, B., & Sunaoǧlu, Ş. K. (2020). Pazarlama bilim dalında yazılmış yüksek lisans ve doktora tezlerinin bibliyometrik profilinin incelenmesi. Third Sector Social Economic Review, 55(4), 2997-3021. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.20.12.1509
  • Kherwa, P., & Bansal, P. (2020). Topic modeling: A comprehensive review. EAI Endorsed Transactions on Scalable Information Systems, 7(24), 1-16. https://doi.org/10.4108/eai.13-7-2018.159623
  • Kim, D. Y., & Kim, S. Y. (2022). The impact of customer-generated evaluation information on sales in online platform-based markets. J. Retail. Consum. Serv., 68(103016), 103016. https://doi.org/10.1016/j.jretconser.2022.103016
  • Kim, M. C., Zhu, Y., Kim, M. C., & Zhu, Y. (2018). Scientometrics of Scientometrics: Mapping historical footprint and emerging technologies in scientometrics. Scientometrics. https://doi.org/10.5772/INTECHOPEN.77951
  • Kolomoyets, Y., & Dickinger, A. (2023). Understanding value perceptions and propositions: A machine learning approach. J. Bus. Res., 154(113355), 113355. https://doi.org/10.1016/j.jbusres.2022.113355
  • Kumar, P. (2022). Managing service flexibility in healthcare for improved customer experience: a data-driven approach. J. Strat. Mark., 1-22. https://doi.org/10.1080/0965254x.2022.2096671
  • Lee, L. W., Dabirian, A., McCarthy, I. P., & Kietzmann, J. (2020). Making sense of text: artificial intelligence-enabled content analysis. Eur. J. Mark., 54(3), 615-644. https://doi.org/10.1108/ejm-02-2019-0219
  • Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881-894. https://doi.org/10.1509/JMKR.48.5.881
  • Lei, S., & Zhang, Y. (2020). The role of the media in socially responsible investing. Int. J. Bank Mark., 38(4), 823-841. https://doi.org/10.1108/ijbm-09-2019-0332
  • Letunovska, N., Lyuolyov, O., Pimonenko, T., & Aleksandrov, V. (2021). Environmental management and social marketing: a bibliometric analysis. E3S Web of Conferences, 234, 00008. https://doi.org/10.1051/E3SCONF/202123400008
  • Li, H., & Ma, L. (2020). Charting the path to purchase using topic models. Journal of Marketing Research, 57(6), 1019-1036. https://doi.org/10.1177/0022243720954376
  • Li, M., Zhao, L., & Srinivas, S. (2023). It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers. Int. J. Consum. Stud. https://doi.org/10.1111/ijcs.12895
  • Lim, W. M., Gupta, G., Biswas, B., & Gupta, R. (2022). Collaborative consumption continuance: a mixed-methods analysis of the service quality-loyalty relationship in ride-sharing services. Electron. Mark., 32(3), 1463-1484. https://doi.org/10.1007/s12525-021-00486-z
  • Liu, L., Tang, L., Dong, W., Yao, S., & Zhou, W. (2016). An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1). https://doi.org/10.1186/S40064-016-3252-8
  • Loureiro, S. M. C., Guerreiro, J., Eloy, S., Langaro, D., & Panchapakesan, P. (2019). Understanding the use of virtual reality in marketing: A text mining-based review. J. Bus. Res., 100, 514-530. https://doi.org/10.1016/j.jbusres.2018.10.055
  • Marshall, P. (2022). A latent allocation model for brand awareness and mindset metrics. Int. J. Mark. Res., 64(4), 526-540. https://doi.org/10.1177/14707853211040052
  • Mathaisel, D. F. X., & Comm, C. L. (2021). Political marketing with data analytics. J. Mark. Anal., 9(1), 56-64. https://doi.org/10.1057/s41270-020-00097-1
  • Meena, P., & Kumar, G. (2022). Online food delivery companies’ performance and consumers expectations during Covid-19: An investigation using machine learning approach. J. Retail. Consum. Serv., 68(103052), 103052. https://doi.org/10.1016/j.jretconser.2022.103052
  • Mifrah, S., & Benlahmar, E. H. (2022). Topic modeling with transformers for sentence-Level using coronavirus corpus. International Journal of Interactive Mobile Technologies (IJIM), 16(17), 50-59. https://doi.org/10.3991/IJIM.V16I17.33281
  • Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. EMNLP 2011-Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 262-272.
  • Mingers, J., Macri, F., & Petrovici, D. A. (2012). Using the h-index to measure the quality of journals in the field of business and management. Information Processing & Management, 2(48), 234-241. https://doi.org/10.1016/j.ipm.2011.03.009
  • Mishra, M. (2022). Customer experience: Extracting topics from tweets. Int. J. Mark. Res., 64(3), 334-353. https://doi.org/10.1177/14707853211047515
  • Mistry, D. A., & Shah, A. (2018). Topic detection in Twitter with data mining. SJ Impact Factor: 6, 887. https://doi.org/10.22214/ijraset.2018.4482
  • Moro, S., Lopes, R. J., Esmerado, J., & Botelho, M. (2020). Service quality in airport hotel chains through the lens of online reviewers. J. Retail. Consum. Serv., 56(102193), 102193. https://doi.org/10.1016/j.jretconser.2020.102193
  • Moro, S., Pires, G., Rita, P., & Cortez, P. (2019). A text mining and topic modelling perspective of ethnic marketing research. Journal of Business Research, 103, 275-285. https://doi.org/10.1016/j.jbusres.2019.01.053
  • Moro, S., Pires, G., Rita, P., & Cortez, P. (2020). A cross-cultural case study of consumers’ communications about a new technological product. J. Bus. Res., 121, 438-447. https://doi.org/10.1016/j.jbusres.2018.08.009
  • Mostafa, M. M. (2019). Clustering halal food consumers: A Twitter sentiment analysis. Int. J. Mark. Res., 61(3), 320-337. https://doi.org/10.1177/1470785318771451
  • Mostafa, M. M. (2021). Information diffusion in halal food social media: A social network approach. J. Int. Consum. Mark., 33(4), 471-491. https://doi.org/10.1080/08961530.2020.1818158
  • Mukherjee, P., Dutta, S., & De Bruyn, A. (2022). Did clickbait crack the code on virality? J. Acad. Mark. Sci., 50(3), 482-502. https://doi.org/10.1007/s11747-021-00830-x
  • Muñoz-Leiva, F., Rodríguez López, M. E., Liebana-Cabanillas, F., & Moro, S. (2021). Past, present, and future research on self-service merchandising: a co-word and text mining approach. Eur. J. Mark., 55(8), 2269-2307. https://doi.org/10.1108/ejm-02-2019-0179
  • Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389-404. https://doi.org/10.1016/J.JBUSRES.2020.10.044
  • Nam, H., Joshi, Y. V., & Kannan, P. K. (2017). Harvesting brand information from social tags. J. Mark., 81(4), 88-108. https://doi.org/10.1509/jm.16.0044
  • Netzer, O., Feldman, R., Goldenberg, J., ve Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543. https://doi.org/10.1
  • Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010). Automatic evaluation of topic coherence. NAACL HLT 2010-Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference, 100-108.
  • Nguyen, D. Q., Billingsley, R., Du, L., & Johnson, M. (2015). Improving topic models with latent feature word representations. Transactions of the Association for Computational Linguistics, 3, 299-313. https://doi.org/10.1162/tacl_a_00140
  • Nikolenko, S. I., Koltcov, S., & Koltsova, O. (2017). Topic modelling for qualitative studies. Article Journal of Information Science, 43(1), 88-102. https://doi.org/10.1177/0165551515617393
  • Pahrudin, P., Liu, L. W., & Li, S. Y. (2022). What is the role of tourism management and marketing toward sustainable tourism? A bibliometric analysis approach. Sustainability 2022, Vol. 14, Page 4226, 14(7), 4226. https://doi.org/10.3390/SU14074226
  • Pal, R., Sekh, A. A., Dogra, D. P., Kar, S., Roy, P. P., & Prasad, D. K. (2021). Topic-based video analysis. ACM Computing Surveys (CSUR), 54(6). https://doi.org/10.1145/3459089
  • Pardo, C., Pagani, M., & Savinien, J. (2022). The strategic role of social media in business-to-business contexts. Ind. Mark. Manag., 101, 82-97. https://doi.org/10.1016/j.indmarman.2021.11.010
  • Park, J., Yang, D., & Kim, H. Y. (2023). Text mining-based four-step framework for smart speaker product improvement and sales planning. J. Retail. Consum. Serv., 71(103186), 103186. https://doi.org/10.1016/j.jretconser.2022.103186
  • Patrick, Z., & Hee, O. C. (2020). A bibliometric analysis of global online marketing research trends. International Journal of Academic Research in Business and Social Sciences, 10(5). https://doi.org/10.6007/IJARBSS/V10-I5/7248
  • Poushneh, A., & Rajabi, R. (2022). Can reviews predict reviewers’ numerical ratings? The underlying mechanisms of customers’ decisions to rate products using Latent Dirichlet Allocation (LDA). Journal Of Consumer Marketing, 39(2), 230-241. https://doi.org/10.1108/JCM-09-2020-4114
  • Puranam, D., Narayan, V., & Kadiyali, V. (2017). The effect of calorie posting regulation on consumer opinion: A flexible latent dirichlet allocation model with informative priors. https://doi.org/10.1287/mksc.2017.1048, 36(5), 726-746. https://doi.org/10.1287/MKSC.2017.1048
  • 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
  • Trusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Mark. Sci., 35(3), 405-426. https://doi.org/10.1287/mksc.2015.0956
  • Vallurupalli, V., & Bose, I. (2020). Exploring thematic composition of online reviews: A topic modeling approach. Electronıc Markets, 30(4), 791-804. https://doi.org/10.1007/s12525-020-00397-5
  • Vanhala, M., Lu, C., Peltonen, J., Sundqvist, S., Nummenmaa, J., & Järvelin, K. (2020). The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research. Journal of Business Research, 106, 46-59. https://doi.org/10.1016/J.JBUSRES.2019.09.009
  • Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/J.IS.2020.101582
  • Wallach, H. M., Mimno, D., & McCallum, A. (2009). Rethinking LDA: Why priors matter. Advances in Neural Information Processing Systems 22-Proceedings of the 2009 Conference, 1973-1981. http://rexa.info/
  • 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
  • Ye, F., Xia, Q., Zhang, M., Zhan, Y., & Li, Y. (2022). Harvesting online reviews to identify the competitor set in a service business: Evidence from the hotel industry. J. Serv. Res., 25(2), 301-327. https://doi.org/10.1177/1094670520975143
  • Yi, F., Jiang, B., ve Wu, J. (2020). Topic modeling for short texts via word embedding and document correlation. IEEE Access, 8, 30692-30705. https://doi.org/10.1109/ACCESS.2020.2973207
  • Zhang, J. (2019). What’s yours is mine: exploring customer voice on Airbnb using text-mining approaches. Journal of Consumer Marketing, 36(5), 655-665. https://doi.org/10.1108/JCM-02-2018-2581
  • Zuo, Y., Wu, J., Zhang, H., Lin, H., Wang, F., Xu, K., & Xiong, H. (2016). Topic modeling of short texts. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2105-2114. https://doi.org/10.1145/2939672.2939880
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

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