The Impact of COVID-19 Pandemic on the Turkish Mobile Gaming Market: A Text Mining Application
Year 2024,
Volume: 8 Issue: 1, 1 - 19, 18.07.2024
Cigdem Kadaifci
,
Cafer Erhan Bozdağ
,
Erkan Işıklı
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.
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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
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method of sentiment and statistical process control analyses. Advanced Engineering Informatics, 49, 101304.
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Araştırma. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 47, 47-58.
https://doi.org/10.52642/susbed.1010309
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players’ reviews. Information, 12(3), 129. https://doi.org/10.3390/info12030129
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framework to improve decision-making processes with text data. International Journal of Hospitality
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- 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.
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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
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FINAL.pdf
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financial performance. Technological and Economic Development of Economy, 26(6), 1422-1443.
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Özel Sayı, 374-380. https://doi.org/10.31590/ejosat.780609
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aspects of customers’ reviews. Journal of Retailing and Consumer Services, 68, 103011.
https://doi.org/10.1016/j.jretconser.2022.103011
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opportunities. Artificial Intelligence Review, 55, 749–800. https://doi.org/10.1007/s10462-021-10043-x
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COVID-19 Pandemisinin Türkiye Mobil Oyun Pazarına Etkisi: Bir Metin Madenciliği Uygulaması
Year 2024,
Volume: 8 Issue: 1, 1 - 19, 18.07.2024
Cigdem Kadaifci
,
Cafer Erhan Bozdağ
,
Erkan Işıklı
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.
References
- 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
- 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
- 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
- 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
- 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
- Churchill, R. ve Singh, L. (2022). The evolution of topic modeling. ACM Computing Surveys, 54(10s), 1-35.
https://doi.org/10.1145/3507900
- 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
- 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
- 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
- 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
- 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
- Gaming in Turkey Oyun ve Espor Ajansı. (t.y.). Türkiye oyun sektörü raporu 2021. Erişim adresi:
https://www.turkiyeoyunsektoruraporu.com/tr/2021-1
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Management, 94, 102853. https://doi.org/10.1016/j.ijhm.2020.102853
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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