How Culture Shapes Customers: A Cross-Continent Analysis of Apps Reviews Using NLP Techniques
Resumo
Understanding customers’ feedback is essential for businesses to improve products and adapt to different markets. This study analyzes 100,000 app reviews from Uber, Instagram, and WhatsApp across six countries (Brazil, U.S., Australia, India, U.K., and South Africa) to assess whether user concerns are global or culturally influenced. Using BERTopic for topic modeling, zero-shot classification for topic assignment, and Aspect-Based Sentiment Analysis, we identify twelve key topics in reviews. While some topics are shared globally, others are country-specific. For example, Uber’s reliability was a major concern in South Africa and Australia, while Brazilian users discussed WhatsApp voice messages more frequently. These findings help businesses detect market-specific trends, benchmark competitors, and address regional needs strategically.
Palavras-chave:
NLP, Bertopic, ABSA, zero shot classification, app reviews
Referências
Ahn, H. and Park, E. (2023). Motivations for user satisfaction of mobile fitness applications: An analysis of user experience based on online review comments. Humanities and Social Sciences Communications, 10(1):1–7.
Amirkhalili, Y. and Wong, H. Y. (2025). Banking on feedback: Text analysis of mobile banking ios and google app reviews. arXiv preprint arXiv:2503.11861.
Aslam, N., Ramay, W. Y., Xia, K., and Sarwar, N. (2020). Convolutional neural network based classification of app reviews. IEEE Access, 8:185619–185628.
Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805.
Fatima, E., Kanwal, H., Khan, J. A., and Khan, N. D. (2024). An exploratory and automated study of sarcasm detection and classification in app stores using fine-tuned deep learning classifiers. Automated Software Engineering, 31(2):69.
Fischer, R. A.-L., Walczuch, R., and Guzman, E. (2021). Does culture matter? impact of individualism and uncertainty avoidance on app reviews. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), pages 67–76. IEEE.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Krishnan, A. (2023). Exploring the power of topic modeling techniques in analyzing customer reviews: a comparative analysis. arXiv preprint arXiv:2308.11520.
Pranatawijaya, V. H., Sari, N. N. K., Rahman, R. A., Christian, E., and Geges, S. (2024). Unveiling user sentiment: Aspect-based analysis and topic modeling of ride-hailing and google play app reviews. Journal of Information Systems Engineering and Business Intelligence, 10(3):328–339.
Santos, G., Mota, V. F. S., Benevenuto, F., and Silva, T. H. (2020). Neutrality may matter: sentiment analysis in reviews of Airbnb, Booking, and Couchsurfing in Brazil and USA. Social Network Analysis and Mining, 10(1):45.
Statista (2025). Annual number of global mobile app downloads 2016-2023. [link].
Amirkhalili, Y. and Wong, H. Y. (2025). Banking on feedback: Text analysis of mobile banking ios and google app reviews. arXiv preprint arXiv:2503.11861.
Aslam, N., Ramay, W. Y., Xia, K., and Sarwar, N. (2020). Convolutional neural network based classification of app reviews. IEEE Access, 8:185619–185628.
Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805.
Fatima, E., Kanwal, H., Khan, J. A., and Khan, N. D. (2024). An exploratory and automated study of sarcasm detection and classification in app stores using fine-tuned deep learning classifiers. Automated Software Engineering, 31(2):69.
Fischer, R. A.-L., Walczuch, R., and Guzman, E. (2021). Does culture matter? impact of individualism and uncertainty avoidance on app reviews. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), pages 67–76. IEEE.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Krishnan, A. (2023). Exploring the power of topic modeling techniques in analyzing customer reviews: a comparative analysis. arXiv preprint arXiv:2308.11520.
Pranatawijaya, V. H., Sari, N. N. K., Rahman, R. A., Christian, E., and Geges, S. (2024). Unveiling user sentiment: Aspect-based analysis and topic modeling of ride-hailing and google play app reviews. Journal of Information Systems Engineering and Business Intelligence, 10(3):328–339.
Santos, G., Mota, V. F. S., Benevenuto, F., and Silva, T. H. (2020). Neutrality may matter: sentiment analysis in reviews of Airbnb, Booking, and Couchsurfing in Brazil and USA. Social Network Analysis and Mining, 10(1):45.
Statista (2025). Annual number of global mobile app downloads 2016-2023. [link].
Publicado
29/09/2025
Como Citar
AZOLIN KOTSIFAS, Maria Fernanda; LÜDERS, Ricardo; SILVA, Thiago H..
How Culture Shapes Customers: A Cross-Continent Analysis of Apps Reviews Using NLP Techniques. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 760-766.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2025.247502.
