The Impact of COVID-19 Pandemic on the Turkish Mobile Gaming Market: A Text Mining Application

Abstract

As its prominence increases in our lives, digital entertainment is becoming an area where customer experience should be monitored more closely. The mobile gaming market has been attracting investments worldwide in recent years since mobile games allow both adults and children to have fun in any place and any time due to their portability and ease of access. The rapid growth trend observed in mobile games due to technological developments has accelerated during the COVID-19 pandemic. Examining how such major transformation shocks affect a market with a high growth potential is critical. Aiming to reveal the impact of the pandemic on the Turkish mobile gaming market, consumer reviews in a specific genre were collected, and how players’ behavior were changed due to the pandemic were compared employing correspondence analysis and topic modeling. The findings of both techniques revealed that the problems and topics discussed during the pandemic differed from those detected before it. The proposed framework, which was employed considering a limited number of games that belonged to a specific genre, can be easily adapted to other games and mobile applications with consumer reviews written in different languages.

Keywords

• mobile games
• text mining
• sentiment analysis
• topic modeling
• correspondence analysis

COVID-19 Pandemisinin Türkiye Mobil Oyun Pazarına Etkisi: Bir Metin Madenciliği Uygulaması

Abstract

Giderek hayatımızda daha büyük yer edinen dijital eğlence, müşteri deneyimi ve davranışlarının yakından izlenmesi gereken bir alan haline gelmiştir. Taşınabilirliği sayesinde insanların herhangi bir mekânda ve zamanda eğlenmesine olanak sağlayan mobil oyunlar, çocuklar kadar yetişkinlere de hitap ederek daha kazançlı hale gelmiş; böylelikle mobil oyun pazarı, küresel ölçekte son yıllarda daha fazla yatırım çekmeye başlamıştır. Teknolojik gelişmeler sayesinde mobil oyunlarda gözlenen hızlı büyüme eğilimi, COVID-19 pandemisi nedeniyle ivme kazanmıştır. Böyle dönüşüm şoklarının büyüme potansiyeli yüksek bir sektörü nasıl etkilediğini incelemek önemlidir. Pandeminin oyun pazarına olan etkisini, oyuncu davranışlarını inceleyerek ortaya koymayı amaçlayan bu çalışmada, belli bir oyun türüne ait Türkçe kullanıcı yorumları toplanmış, kullanıcıların pandemi öncesindeki ve sonrasındaki duyguları uyum analizi ve konu modellemesi sayesinde karşılaştırılmıştır. Her iki yöntem de pandemi sonrasında öne çıkan sorunların ve konu başlıklarının pandemi öncesine kıyasla farklılaştığını ortaya koymaktadır. Tek bir türden kısıtlı sayıda oyun göz önünde bulundurularak uygulanan metodolojik çerçeve, farklı dillerde yazılmış yorumlara sahip başka oyunlara ve mobil uygulamalara da kolaylıkla uyarlanabilir.

Keywords

• mobil oyunlar
• metin madenciliği
• duygu analizi
• konu modellemesi
• uyum analizi

References

1. Acheampong, F. A., Nunoo-Mensah, H. ve Chen, W. (2021). Transformer models for text-based emotion detection: a review of BERT-based approaches. Artificial Intelligence Review, 54(8), 5789-5829. https://doi.org/10.1007/s10462-021-09958-2
2. Acikalin, U. U., Bardak, B. ve Kutlu, M. (2020). Turkish sentiment analysis using BERT. 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Türkiye, 5-7 October 2020. https://doi:10.1109/SIU49456.2020.9302492
3. Ahmetoğlu, H. ve Daş, R. (2020). Türkçe otel yorumlarıyla eğitilen kelime vektörü modellerinin duygu analizi ile incelenmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 24(2), 455-463. https://doi.org/10.19113/sdufenbed.645579
4. Altınel, A. B. (2022). Türkçe metinlerde makine öğrenmesi algoritmalarının duygu analizi problemi üzerindeki performansının kıyaslanması. Avrupa Bilim ve Teknoloji Dergisi, 28, 1056-1061. https://doi.org/10.31590/ejosat.1011864 B2PRESS (2020, Mayıs 6). Pandemi Oyun Sektörünün Gelişimini Nasıl Etkiledi? Erişim adresi: https://tr.b2press.com/kutuphane/23/pandemi-oyun-sektorunun-gelisimini-nasil-etkiledi
5. Barr, M. ve Copeland-Stewart, A. (2022). Playing video games during the COVID-19 pandemic and effects on players’ well-being. Games and Culture, 17(1), 122-139. https://doi.org/10.1177/15554120211017036 Che, S., Nan, D., Kamphuis, P., Zhang, S. ve Kim, J. H. (2022). Examining Crisis Communication Using Semantic Network and Sentiment Analysis: A Case Study on NetEase Games. Frontiers in Psychology, 13, 823415. https://doi.org/10.3389/fpsyg.2022.823415
6. Churchill, R. ve Singh, L. (2022). The evolution of topic modeling. ACM Computing Surveys, 54(10s), 1-35. https://doi.org/10.1145/3507900
7. Ciftci, B. ve Apaydin, M. S. (2018). A deep learning approach to sentiment analysis in Turkish. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Türkiye, 28-30 September 2018. https://doi.org/10.1109/IDAP.2018.8620751
8. de Santana Correia, A. ve Colombini, E. L. (2022). Attention, please! A survey of neural attention models in deep learning. Artificial Intelligence Review, 55(8), 6037-6124, 2022. https://doi.org/10.1007/s10462-022-10148-x

9. Deepa, D. ve Tamilarasi, A. (2021). Bidirectional encoder representations from transformers (BERT) language model for sentiment analysis task. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(7), 1708-1721. https://doi.org/10.17762/turcomat.v12i7.3055
10. Demirci, G. M., Keskin, Ş. R. ve Doğan, G. (2019). Sentiment analysis in Turkish with deep learning. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9-12 December 2019. https://doi.org/10.1109/BigData47090.2019.9006066
11. Devlin, J. ve Chang, M.-W. (2018, 2 Kasım). Open sourcing BERT: State-of-the-Art pre-training for natural language processing [Blog yazısı]. Erişim adresi: https://ai.googleblog.com/2018/11/open-sourcing-bert-state-ofart- pre.html
12. Gaming in Turkey Oyun ve Espor Ajansı. (t.y.). Türkiye oyun sektörü raporu 2021. Erişim adresi: https://www.turkiyeoyunsektoruraporu.com/tr/2021-1
13. Godnov, U. ve Redek, T. (2016). Application of text mining in tourism: Case of Croatia. Annals of Tourism Research, 58, 162-166. https://doi.org/10.1016/j.annals.2016.02.005
14. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. ve Tatham, R. L. (2014). Multivariate Data Analysis (7th ed.). Essex, UK: Pearson Education Limited.
15. Han, Y. ve Moghaddam, M. (2021). Analysis of sentiment expressions for user-centered design. Expert Systems with Applications, 171, 114604. https://doi.org/10.1016/j.eswa.2021.114604
16. Hoffman D. L. ve Franke, G. R. (1986). Correspondence analysis: graphical representation of categorical data in marketing research. Journal of Marketing Research, 23(3), 213-227. https://doi.org/10.1177/002224378602300302
17. Hossain, M. S. ve Rahman, M. F. (2022). Detection of potential customers’ empathy behavior towards customers’ reviews. Journal of Retailing and Consumer Services, 65, 102881. https://doi.org/10.1016/j.jretconser.2021.102881
18. Işıklı, E. (2021). Metin madenciliğinin talep planlamadaki rolünün incelenmesi. Endüstri Mühendisliği, 32(2), 286-306. https://doi.org/10.46465/endustrimuhendisligi.796901 Kilimci, Z. H., Yörük, H. ve Akyokus, S. (2020). Sentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithms. 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Novi Sad, Serbia, 24-26 August 2020. https://doi.org/10.1109/INISTA49547.2020.919462
19. Kim, J. ve Lim, C. (2021). Customer complaints monitoring with customer review data analytics: An integrated method of sentiment and statistical process control analyses. Advanced Engineering Informatics, 49, 101304. https://doi.org/10.1016/j.aei.2021.101304
20. Küçükvardar, M. ve Türel, E. (2022). Covid-19 Pandemisinde Dijital Oyun Oynama Düzeyi Üzerine Bir Araştırma. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 47, 47-58. https://doi.org/10.52642/susbed.1010309
21. Li, X., Zhang, Z. ve Stefanidis, K. (2021). A data-driven approach for video game playability analysis based on players’ reviews. Information, 12(3), 129. https://doi.org/10.3390/info12030129
22. Marcolin, C. B., Becker, J. L., Wild, F., Behr, A. ve Schiavi, G. (2021). Listening to the voice of the guest: A framework to improve decision-making processes with text data. International Journal of Hospitality
23. Management, 94, 102853. https://doi.org/10.1016/j.ijhm.2020.102853
24. Masarifoglu, M., Tigrak, U., Hakyemez, S., Gul, G., Bozan, E., Buyuklu, A. H. ve Özgür, A. (2021). Sentiment analysis of customer comments in banking using BERT-based approaches. 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, 9-11 June 2021.
25. https://doi.org/10.1109/SIU53274.2021.9477890 Mehta, M. P., Kumar, G. ve Ramkumar, M. (2021). Customer expectations in the hotel industry during the COVID-19 pandemic: A global perspective using sentiment analysis. Tourism Recreation Research, 48(1), 110- 127. https://doi.org/10.1080/02508281.2021.189469
26. Motion Picture Association. (2022, Mart). Theatrical and home entertainment market environment THEME report 2021. Erişim adresi: https://www.motionpictures.org/wp-content/uploads/2022/03/MPA-2021-THEME-Report- FINAL.pdf
27. Myšková, R. ve Hájek, P. (2020). Mining risk-related sentiment in corporate annual reports and its effect on financial performance. Technological and Economic Development of Economy, 26(6), 1422-1443. https://doi.org/10.3846/tede.2020.13758
28. Onan, A. (2020). Evrişimli sinir ağı mimarilerine dayalı Türkçe duygu analizi. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 374-380. https://doi.org/10.31590/ejosat.780609 Pashchenko, Y., Rahman, M. F., Hossain, M. S., Uddin, M. K. ve Islam, T. (2022). Emotional and the normative aspects of customers’ reviews. Journal of Retailing and Consumer Services, 68, 103011. https://doi.org/10.1016/j.jretconser.2022.103011
29. Qazi, N. ve Wong, B. W. (2019). An interactive human centered data science approach towards crime pattern analysis. Information Processing & Management, 56(6), 102066. https://doi.org/10.1016/j.ipm.2019.102066
30. Qu, S., Zhang, Y., Ji, Y., Wang, Z. ve Geng, R. (2023). Online-Review-Driven Products Ranking: A Hybrid Approach. Systems, 11(3), 148. https://doi.org/10.3390/systems11030148
31. Raza, S. ve Ding, C. (2022). News recommender system: A review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55, 749–800. https://doi.org/10.1007/s10462-021-10043-x
32. Ren, Y. ve Li, J. (2020). Research on user satisfaction of mobile game in Chinese style based on sentiment analysis. International Conference on Economics, Education and Social Research (ICEESR 2020), Lanzhou, China, 25-26 July 2020. https://doi.org/10.25236/iceesr.2020.186
33. Schmiedel, T., Müller, O. ve Vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941-968. https://doi.org/10.1177/1094428118773858
34. Sensor Tower (2022). The state of mobile game monetization 2022-An analysis of the latest mobile game monetization strategies. Erişim adresi: https://go.sensortower.com/rs/351-RWH-315/images/st-state-of-mobilegame- monetization-2022.pdf
35. Siğirci, İ. O., Özgür, H., Oluk, A., Uz, H., Çetiner, E., Oktay, H. U. ve Erdemir, K. (2020). Sentiment analysis of Turkish reviews on Google Play Store. 2020 5th International Conference on Computer Science and Engineering (UBMK), Diyarbakır, Türkiye, 9-11 September 2020. https://doi.org/10.1109/UBMK50275.2020.9219407
36. Smirke, R. (2022, 22 Mart). IFPI global report 2022: Music revenues rise for seventh straight year to $25.9B [PDF belgesi]. Erişim adresi: https://www.billboard.com/wp-content/uploads/2022/03/march-22-2022-billboardbulletin. pdf The Gaming Market in Turkey. (2022, 5 Mart). Erişim adresi: https://allcorrectgames.com/insights/the-turkishgame- market
37. Tuzcu, S. (2020). Çevrimiçi kullanıcı yorumlarının duygu analizi ile sınıflandırılması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 1(2), 1-5.
38. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. ve Polosukhin, I. (2017). Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4-9 December 2017.
39. Wang, X. ve Goh, D. H.-L. (2020). Components of game experience: An automatic text analysis of online reviews. Entertainment Computing, 33, 100338. https://doi.org/10.1016/j.entcom.2019.100338
40. Wang, X., Zheng, J., Tang, L. R. ve Luo, Y. (2023). Recommend or not? The influence of emotions on passengers’ intention of airline recommendation during COVID-19. Tourism Management, 95, 104675. https://doi.org/10.1016/j.tourman.2022.104675
41. Wijman, T. (2021, 22 Aralık). The games market and beyond in 2021: the year in numbers [Blog yazısı]. Erişim adresi: https://newzoo.com/insights/articles/the-games-market-in-2021-the-year-in-numbers-esports-cloudgaming
42. Xu, H., Zhang, Y. ve DeGroof, R. (2018). A feature-based sentence model for evaluation of similar online products. Journal of Electronic Commerce Research, 19(4), 320-335.
43. Yu, Y., Dinh, D. T., Nguyen, B. H., Yu, F. ve Huynh, V. N. (2023). Mining Insights from Esports Game Reviews with an Aspect-Based Sentiment Analysis Framework. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3285864