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MOBİLDE YAPAY ZEKÂ SANATI: ÜRETKEN YAPAY ZEKÂ UYGULAMALARI KULLANICI YORUMLARININ KONU MODELLEMESİ

Year 2024, Volume: 20 Issue: ICMEB'24 Özel Sayı, 101 - 113, 30.10.2024
https://doi.org/10.17130/ijmeb.1498188

Abstract

Kullanıcıların istemler aracılığıyla metin, resim ve video içeriği oluşturmasına olanak tanıyan üretken yapay zekâ kavramı, pazarlamada içerik tarafında ve yapay zekâ uygulamalarında devrim yaratmaktadır. Üretken yapay zekâ uygulamalarının artan popülaritesine rağmen, üretken yapay zekaya ilişkin pazar algısı yeterince araştırılmamış durumdadır. Bu çalışma, kullanıcı incelemeleri üzerinden mobil uygulamalar bağlamında üretken yapay zekâ pazarı algısını keşfetmeyi amaçlamaktadır. Çalışma, üretken yapay zekâ mobil uygulamalarının belirlenmesi, mobil uygulamaların derecelendirme puanları ve kurulum miktarları yoluyla bağlamın değerlendirilmesi ve sohbete dahil edilen konuların belirlenmesi amacıyla çevrimiçi incelemeler için bir konu modelleme yaklaşımının (BerTopic) kullanılması dahil olmak üzere yapılandırılmış bir yaklaşımı izlemektedir. Araştırmanın örneklemi olarak 22 mobil uygulamadan 8159 kullanıcı yorumu kullanılmış ve örneklemin ortalama derecelendirme puanı 4,06 olarak bulunmuş ve bu da pazar algısının olumlu olduğuna işaret etmektedir. Çalışmada ilk 10 konu olarak; “Uygulamadaki Reklamların Miktarı, Müstehcen (NSFW) İçerik ve Denetimi, “Uygulamanın Övülmesi”, “İşlevsellik sorunları ve Çökmeler”, “Ödeme Gerekliliği ve Deneme Sorunları”, “Uygulama İçi Satın Alımların Geri Yükleme Sorunları”, “Uygulamadaki Belirli Bir Özellik”, “Sohbet İşlevi”, “Kredi sistemi”, “Reklamların Fazlalığı” tespit edilmiştir. Çalışma, yapay zekâ sanatı mobil uygulamalarının pazarlama karar alma süreçlerine yönelik temel konularını ortaya koymaktadır.

References

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  • Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47(4), 398-411.
  • Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P.N., Inkpen, K.M., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of The 2019 Chi Conference on Human Factors in Computing Systems, (1-13), Scotland.
  • Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
  • Barbosa, R. R. L., Sánchez-Alonso, S., & Sicilia-Urban, M. A. (2015). Evaluating hotels rating prediction based on sentiment analysis services. Aslib Journal of Information Management, 67(4), 392-407.
  • Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24.
  • Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31-40.
  • Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
  • Catsaros, O. (2023). Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds. Retrieved from https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds/ Accessed 15.05.2024.
  • Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477-491.
  • Civitai. (2024). Civitai: The Home of Open-Source Generative AI. Retrieved from https://civitai.com/ Accessed 16.05.2024.
  • Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407-1424.
  • Duan, Y., Liu, T., & Mao, Z. (2022). How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform. Journal of Retailing and Consumer Services, 65, 1-15.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative ai. Business & Information Systems Engineering, 66(1), 111-126.
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  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems, 27.
  • Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
  • Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.
  • Guo, Y., Wang, F., Xing, C., & Lu, X. (2022). Mining multi-brand characteristics from online reviews for competitive analysis: A brand joint model using latent Dirichlet allocation. Electronic Commerce Research and Applications, 53, 1-11.
  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52.
  • Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42-53.
  • Ireland, R., & Liu, A. (2018). Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP Journal of Manufacturing Science and Technology, 23, 128-144.
  • Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77, 1-14.
  • Jo, M. (2019). Google-Play-Scraper. Retrieved from https://github.com/JoMingyu/google-play-scraper. Accessed 16.05.2024.
  • Ko, H. K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023, March). Large-scale text-to-image generation models for visual artists’ creative works. In Proceedings of The 28th International Conference on Intelligent User Interfaces, (919-933), Australia.
  • Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems With Applications, 116, 472-486.
  • Li, X., Wu, C., & Mai, F. (2019). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56(2), 172-184.
  • Li, H., Bruce, X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 1-16.
  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458-468.
  • Lucini, F. R., Tonetto, L. M., Fogliatto, F. S., & Anzanello, M. J. (2020). Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. Journal of Air Transport Management, 83, 1-12.
  • Ma, G., Ma, J., Li, H., Wang, Y., Wang, Z., & Zhang, B. (2022). Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy, 165, 1-10.
  • McKinsey & Company. (2023). What is generative AI?. Retrieved from https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai. Accessed 17.05.2024.
  • Meddeb, A., & Romdhane, L. B. (2022). Using topic modeling and word embedding for topic extraction in Twitter. Procedia Computer Science, 207, 790-799.
  • Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 185-200.
  • Neirotti, P., Raguseo, E., & Paolucci, E. (2016). Are customers’ reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning. International Journal of Information Management, 36(6), 1133-1143.
  • Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
  • Oh, M. M., & Kim, S. S. (2020). Dimensionality of ethnic food fine dining experience: An application of semantic network analysis. Tourism Management Perspectives, 35, 1-13.
  • Oh, M., Badu Baiden, F., Kim, S., & Lema, J. (2023). Identification of delighters and frustrators in vegan-friendly restaurant experiences via semantic network analysis: Evidence from online reviews. International Journal of Hospitality & Tourism Administration, 24(2), 260-287.
  • Oppenlaender, J. (2023). A taxonomy of prompt modifiers for text-to-image generation. Behaviour & Information Technology, 1-14.
  • Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148.
  • Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013-1022.
  • Patel, A., Oza, P., & Agrawal, S. (2023). Sentiment analysis of customer feedback and reviews for airline services using language representation model. Procedia Computer Science, 218, 2459-2467.
  • Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021, July). Learning transferable visual models from natural language supervision. In International Conference On Machine Learning, (8748-8763).
  • Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021, July). Zero-shot text-to-image generation. In International Conference on Machine Learning, (8821-8831).
  • Rani, S., & Kumar, M. (2021). Topic modeling and its applications in materials science and engineering. Materials Today: Proceedings, 45, 5591-5596.
  • Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing, 98, 1-18.
  • Sætra, H. S. (2023). Generative AI: Here to stay, but for good?. Technology in Society, 75, 1-5.
  • Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52-63.
  • Stability AI. (2024). Retrieved from https://stability.ai/stable-image. Accessed 16.05.2024.
  • Stamolampros, P., Korfiatis, N., Kourouthanassis, P., & Symitsi, E. (2019). Flying to quality: Cultural influences on online reviews. Journal of Travel Research, 58(3), 496-511. Van Rossum, G., & Drake, F. L. (1995). Python reference manual. Amsterdam: Open documents library.
  • Weisz, J. D., Muller, M., He, J., & Houde, S. (2023). Toward general design principles for generative AI applications. arXiv preprint arXiv:2301.05578.
  • Westbrook, R. A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of Marketing Research, 24(3), 258-270.
  • Xu, X. (2020). How do consumers in the sharing economy value sharing? Evidence from online reviews. Decision Support Systems, 128, 113162.
  • Zhang, J., Lu, X., & Liu, D. (2021). Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews. Electronic Commerce Research and Applications, 49, 101094.
  • Zhao, S. (2021). Thumb up or down? A text‐mining approach of understanding consumers through reviews. Decision Sciences, 52(3), 699-719.

AI ART IN MOBILE: TOPIC MODELING OF USER REVIEWS FOR GENERATIVE AI APPLICATIONS

Year 2024, Volume: 20 Issue: ICMEB'24 Özel Sayı, 101 - 113, 30.10.2024
https://doi.org/10.17130/ijmeb.1498188

Abstract

The generative AI concept, which enables users to create text, image, and video content through prompts, is revolutionizing the content side and AI applications in marketing. Despite the increasing popularity of generative AI applications, the market perception regarding generative AI remains underexplored. This study aims to explore the generative AI market perception through the context of mobile applications with the help of user reviews. The study follows a structured approach including identifying the generative AI mobile applications, assessing the context through rating scores and install amounts of mobile applications, and using a topic modeling approach (BerTopic) for online reviews to identify the topics included in the conversation. 8159 user reviews from 22 mobile applications are used as sample of the study and the average rating score for the sample found as 4,06 which signals a positive perception of market. The study concludes top ten topics as; “Dissatisfaction About Amount of Advertisements in app”, “NSFW Content and Moderation”, “Praise of Application”, “Functionality Problems & Crashes”, “Payment Necessity and Trial Problems”, “In-App Purchase Restoration Problems”, “Specific Feature in App”, “Chat Function”, “Credit System” and “Excess of Ads”. The study reveals the main issues of Ai Art mobile applications for marketing decision-making processes.

References

  • Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers and citizens in an AI-driven future. Computers and Education: Artificial Intelligence, 2, 1-11.
  • Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47(4), 398-411.
  • Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P.N., Inkpen, K.M., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of The 2019 Chi Conference on Human Factors in Computing Systems, (1-13), Scotland.
  • Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
  • Barbosa, R. R. L., Sánchez-Alonso, S., & Sicilia-Urban, M. A. (2015). Evaluating hotels rating prediction based on sentiment analysis services. Aslib Journal of Information Management, 67(4), 392-407.
  • Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24.
  • Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31-40.
  • Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
  • Catsaros, O. (2023). Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds. Retrieved from https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds/ Accessed 15.05.2024.
  • Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477-491.
  • Civitai. (2024). Civitai: The Home of Open-Source Generative AI. Retrieved from https://civitai.com/ Accessed 16.05.2024.
  • Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407-1424.
  • Duan, Y., Liu, T., & Mao, Z. (2022). How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform. Journal of Retailing and Consumer Services, 65, 1-15.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative ai. Business & Information Systems Engineering, 66(1), 111-126.
  • Goldman Sachs. (2023). Generative AI could raise global GDP by 7%. Retrieved from https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html. Accessed 17.05.2024.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems, 27.
  • Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
  • Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.
  • Guo, Y., Wang, F., Xing, C., & Lu, X. (2022). Mining multi-brand characteristics from online reviews for competitive analysis: A brand joint model using latent Dirichlet allocation. Electronic Commerce Research and Applications, 53, 1-11.
  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52.
  • Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42-53.
  • Ireland, R., & Liu, A. (2018). Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP Journal of Manufacturing Science and Technology, 23, 128-144.
  • Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77, 1-14.
  • Jo, M. (2019). Google-Play-Scraper. Retrieved from https://github.com/JoMingyu/google-play-scraper. Accessed 16.05.2024.
  • Ko, H. K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023, March). Large-scale text-to-image generation models for visual artists’ creative works. In Proceedings of The 28th International Conference on Intelligent User Interfaces, (919-933), Australia.
  • Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems With Applications, 116, 472-486.
  • Li, X., Wu, C., & Mai, F. (2019). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56(2), 172-184.
  • Li, H., Bruce, X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 1-16.
  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458-468.
  • Lucini, F. R., Tonetto, L. M., Fogliatto, F. S., & Anzanello, M. J. (2020). Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. Journal of Air Transport Management, 83, 1-12.
  • Ma, G., Ma, J., Li, H., Wang, Y., Wang, Z., & Zhang, B. (2022). Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy, 165, 1-10.
  • McKinsey & Company. (2023). What is generative AI?. Retrieved from https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai. Accessed 17.05.2024.
  • Meddeb, A., & Romdhane, L. B. (2022). Using topic modeling and word embedding for topic extraction in Twitter. Procedia Computer Science, 207, 790-799.
  • Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 185-200.
  • Neirotti, P., Raguseo, E., & Paolucci, E. (2016). Are customers’ reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning. International Journal of Information Management, 36(6), 1133-1143.
  • Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
  • Oh, M. M., & Kim, S. S. (2020). Dimensionality of ethnic food fine dining experience: An application of semantic network analysis. Tourism Management Perspectives, 35, 1-13.
  • Oh, M., Badu Baiden, F., Kim, S., & Lema, J. (2023). Identification of delighters and frustrators in vegan-friendly restaurant experiences via semantic network analysis: Evidence from online reviews. International Journal of Hospitality & Tourism Administration, 24(2), 260-287.
  • Oppenlaender, J. (2023). A taxonomy of prompt modifiers for text-to-image generation. Behaviour & Information Technology, 1-14.
  • Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148.
  • Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013-1022.
  • Patel, A., Oza, P., & Agrawal, S. (2023). Sentiment analysis of customer feedback and reviews for airline services using language representation model. Procedia Computer Science, 218, 2459-2467.
  • Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021, July). Learning transferable visual models from natural language supervision. In International Conference On Machine Learning, (8748-8763).
  • Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021, July). Zero-shot text-to-image generation. In International Conference on Machine Learning, (8821-8831).
  • Rani, S., & Kumar, M. (2021). Topic modeling and its applications in materials science and engineering. Materials Today: Proceedings, 45, 5591-5596.
  • Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing, 98, 1-18.
  • Sætra, H. S. (2023). Generative AI: Here to stay, but for good?. Technology in Society, 75, 1-5.
  • Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52-63.
  • Stability AI. (2024). Retrieved from https://stability.ai/stable-image. Accessed 16.05.2024.
  • Stamolampros, P., Korfiatis, N., Kourouthanassis, P., & Symitsi, E. (2019). Flying to quality: Cultural influences on online reviews. Journal of Travel Research, 58(3), 496-511. Van Rossum, G., & Drake, F. L. (1995). Python reference manual. Amsterdam: Open documents library.
  • Weisz, J. D., Muller, M., He, J., & Houde, S. (2023). Toward general design principles for generative AI applications. arXiv preprint arXiv:2301.05578.
  • Westbrook, R. A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of Marketing Research, 24(3), 258-270.
  • Xu, X. (2020). How do consumers in the sharing economy value sharing? Evidence from online reviews. Decision Support Systems, 128, 113162.
  • Zhang, J., Lu, X., & Liu, D. (2021). Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews. Electronic Commerce Research and Applications, 49, 101094.
  • Zhao, S. (2021). Thumb up or down? A text‐mining approach of understanding consumers through reviews. Decision Sciences, 52(3), 699-719.
There are 55 citations in total.

Details

Primary Language English
Subjects Business Systems in Context (Other)
Journal Section Research Articles
Authors

Fatih Pınarbaşı 0000-0001-9005-0324

Early Pub Date October 24, 2024
Publication Date October 30, 2024
Submission Date June 8, 2024
Acceptance Date September 18, 2024
Published in Issue Year 2024 Volume: 20 Issue: ICMEB'24 Özel Sayı

Cite

APA Pınarbaşı, F. (2024). AI ART IN MOBILE: TOPIC MODELING OF USER REVIEWS FOR GENERATIVE AI APPLICATIONS. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 20(ICMEB’24 Özel Sayı), 101-113. https://doi.org/10.17130/ijmeb.1498188