Dergiler / Erzincan Binali Yıldırım Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2025 / Cilt: 7 Sayı: 2

METİN MADENCİLİĞİ VE WENSLO-LODECI YÖNTEMLERİ İLE KULLANICI DENEYİMİ ANALİZİ: KARGO MOBİL UYGULAMALARI ÖRNEGİ

USER EXPERIENCE ANALYSIS USING TEXT MINING AND THE WENSLO-LODECI METHODS: THE CASE OF MOBILE CARGO APPLICATIONS

Alparslan Oğuz(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0003-1920-5674
Yunus Emre Özenoğlu(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0001-5051-4622
Okan Alkan(Erzincan Binali Yıldırım Üniversitesi)0000-0002-6526-1645
Sayfa
149–167
Yayın Tarihi
2025-12-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Elde edilen bulgular, özellikle TrendyolExpress’in kullanıcı memnuniyetinde diğer firmalara göre belirgin bir üstünlük sağladığını gösterirken Hepsijet ve Yurtiçi Kargo’nun iyileştirme alanları bulunduğunu ortaya koymaktadır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu çalışmada, kargo firmalarının mobil uygulamalarında kullanıcı deneyimleri metin madenciliği ve çok kriterli karar verme (ÇKKV) yöntemleri ile analiz edilmiştir.
📊
Temel Bulgular & Veri:Google Play ve App Store üzerinden toplanan kullanıcı yorumları, performans kriterlerinin belirlenmesi ve stratejik önerilerin geliştirilmesi amacıyla detaylı bir şekilde incelenmiştir.
💡
Sonuç & Katkı:Elde edilen bulgular, özellikle TrendyolExpress’in kullanıcı memnuniyetinde diğer firmalara göre belirgin bir üstünlük sağladığını gösterirken Hepsijet ve Yurtiçi Kargo’nun iyileştirme alanları bulunduğunu ortaya koymaktadır.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Metin Madenciliği · Kullanıcı Deneyimi · Çok Kriterli Karar Verme

Özet

Bu çalışmada, kargo firmalarının mobil uygulamalarında kullanıcı deneyimleri metin madenciliği ve çok kriterli karar verme (ÇKKV) yöntemleri ile analiz edilmiştir. Google Play ve App Store üzerinden toplanan kullanıcı yorumları, performans kriterlerinin belirlenmesi ve stratejik önerilerin geliştirilmesi amacıyla detaylı bir şekilde incelenmiştir. Elde edilen bulgular, özellikle TrendyolExpress’in kullanıcı memnuniyetinde diğer firmalara göre belirgin bir üstünlük sağladığını gösterirken Hepsijet ve Yurtiçi Kargo’nun iyileştirme alanları bulunduğunu ortaya koymaktadır. Metin madenciliği ile yapılan bigram analizleri, kullanıcı deneyimlerini derinlemesine anlamada önemli bir araç olarak işlev görmektedir. WENSLO ve LODECI yöntemlerinin uygulanması sonucunda SDB ve TP+ kriterlerinin, müşteri memnuniyetinin artırılmasında belirleyici olduğu tespit edilmiştir. Sonuç olarak bu çalışma, kargo firmalarının kullanıcı deneyimlerini geliştirmeye yönelik stratejik süreçlerde kullanılabilecek somut ve bilimsel bir temel sunmaktadır.

Abstract

This study analyzes user experiences in the mobile applications of cargo companies by integrating text mining and multi-criteria decision-making (MCDM) methods. User reviews collected from Google Play and the App Store were examined in detail to identify performance criteria and to develop strategic recommendations. The findings indicate that TrendyolExpress exhibits a notable superiority over other firms in terms of user satisfaction, while Hepsijet and Yurtiçi Kargo have identifiable areas for improvement. Bigram analyses conducted through text mining functioned as an effective tool for gaining an in-depth understanding of user experiences. As a result of applying the WENSLO and LODECI methods, it was determined that the SDB and TP+ criteria are the most decisive factors in enhancing customer satisfaction. In conclusion, this study provides a concrete and scientific basis that can be utilized in strategic processes aimed at improving user experiences in cargo companies.

🔖

Bu Makaleye Atıf Yap

Oğuz, A., Özenoğlu, Y. E., Alkan, O. (2025). METİN MADENCİLİĞİ VE WENSLO-LODECI YÖNTEMLERİ İLE KULLANICI DENEYİMİ ANALİZİ: KARGO MOBİL UYGULAMALARI ÖRNEGİ. Erzincan Binali Yıldırım Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 7 Sayı: 2, 149–167. https://doi.org/10.46482/ebyuiibfdergi.1816735

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (39)

  1. Abdeen, M., Hamed, A., & Wu, X. (2021). Fighting the covid-19 infodemic in new articles and false publications: neonet, a text-based supervised machine learning algorithm. https://doi.org/10.20944/preprints202106.0482.v2
  2. Adyatma, A., Afuan, L., & Maryanto, E. (2023). The effect of unigram and bigram in the naïve bayes multinomial for analyzing of comment sentiment of gojek application in google play store. Jurnal Teknik Informatika (Jutif), 4(6), 1535-1540. https://doi.org/10.52436/1.jutif.2023.4.6.1310
  3. Albijanić, I., Milošević, M., & Jeremić, V. (2022). Exploring the factors which impact the customers’ online purchase intentions. Proceedings of the 4th International Conference on Statistics: Theory and Applications. https://doi.org/10.11159/icsta22.102
  4. Algamash, F. A., Mashi, M. S., & Alam, M. N. (2022). Understanding the antecedents of use of e-commerce and consumers’ e-loyalty in Saudi Arabia amid the COVID-19 pandemic. Sustainability, 14(22), 14894. https://doi.org/10.3390/su142214894
  5. Altrabsheh, N., Kontonatsios, G., & Korkontzelos, I. (2019). Evaluating the accuracy and efficiency of sentiment analysis pipelines with uima., 286-294. https://doi.org/10.1007/978-3-030-23281-8_23
  6. Bani-Doumi, M., Serrano-Guerrero, J., Chiclana, F., Romero, F. P., & Olivas, J. A. (2024). A picture fuzzy set multi criteria decision-making approach to customize hospital recommendations based on patient feedback. Applied Soft Computing, 153, 111331. https://doi.org/10.1016/j.asoc.2024.111331
  7. Chen, C. H., & Feng-lin, W. (2023). Exploring the innovative application of Azure cloud computing platform in cross-border e-commerce operation. Frontiers in Computing and Intelligent Systems, 4(2), 21–26. https://doi.org/10.54097/fcis.v4i2.9746
  8. Cheng, C., Sakai, T., Alho, A. R., Cheah, L., & Ben‐Akiva, M. (2021). Exploring the relationship between locational and household characteristics and e-commerce home delivery demand. Logistics, 5(2), 29. https://doi.org/10.3390/logistics5020029
  9. Çalı, S., & Balaman, Ş. Y. (2019). Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Computers & Industrial Engineering, 129, 315–332. https://doi.org/10.1016/j.cie.2019.01.051
  10. Fan, Z.-P., Xi, Y., & Liu, Y. (2018). Supporting consumer’s purchase decision: A method for ranking products based on online multi-attribute product ratings. Soft Computing, 22(16), 5247–5261. https://doi.org/10.1007/s00500-017-2961-4
  11. Heidary Dahooie, J., Raafat, R., Qorbani, A. R., & Daim, T. (2021). An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making. Technological Forecasting and Social Change, 173, 121158. https://doi.org/10.1016/j.techfore.2021.121158
  12. Hu, J., Zhang, X., Yang, Y., Liu, Y., & Chen, X. (2020). New doctors ranking system based on VIKOR method. International Transactions in Operational Research, 27(2), 1236–1261. https://doi.org/10.1111/itor.12569
  13. Indrawan, G., Setiawan, H., & Gunadi, A. (2022). Multi-class svm classification comparison for health service satisfaction survey data in bahasa. Hightech and Innovation Journal, 3(4), 425-442. https://doi.org/10.28991/hij-2022-03-04-05
  14. Ji, H., Yang, S., Jia, B., Zhang, M., & Su, B. (2024). Study on location selection of urban two-level joint express delivery stations considering fair cost allocation among enterprises. Transportation Research Record: Journal of the Transportation Research Board, 2678(10), 1551–1568. https://doi.org/10.1177/03611981241239651
  15. Ji, P., Zhang, H.-Y., & Wang, J.-Q. (2019). A fuzzy decision support model with sentiment analysis for items comparison in e-commerce: The case study of http://PConline.com. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(10), 1993–2004. https://doi.org/10.1109/TSMC.2018.2875163
  16. Laksono, S. I., Aritonang, Y. K., & Lesmono, D. (2020). The model for location routing problem with roaming delivery locations. Jurnal Teknik Industri, 21(2), 174–184. https://doi.org/10.22219/jtiumm.vol21.no2.174-184
  17. Liang, R., & Wang, J. (2019). A linguistic intuitionistic cloud decision support model with sentiment analysis for product selection in e-commerce. International Journal of Fuzzy Systems, 21(3), 963–977. https://doi.org/10.1007/s40815-019-00606-0
  18. Lu, H., Ning, K., Li, Y., Zhan, Q., Xie, J., & Wang, C. (2019). Utilizing recurrent neural network for topic discovery in short text scenarios1. Intelligent Data Analysis, 23(2), 259-277. https://doi.org/10.3233/ida-183842
  19. Mock, W. Y. (2022). Rise of online shopping in Shopee: Is Shopee Xpress satisfying to consumers? International Journal of Tourism & Hospitality in Asia Pasific, 5(3), 105–116. https://doi.org/10.32535/ijthap.v5i3.1903
  20. Ng, C., Lam, S., & Liu, K. (2022). Sentiment analysis on consumers’ opinions – Evaluating online retailers through analyzing sentiment for face masks during COVID-19 pandemic. Journal of Industrial and Production Engineering, 39(7), 535–551. https://doi.org/10.1080/21681015.2022.2070933
  21. Pala, O., Atçeken, Ö., Omurtak, H., & Şimşir, B. (2024). BİST çimento sektöründe LODECI ve CRADIS ile finansal performans analizi. Alanya Akademik Bakış, 8(3), 956-970.
  22. Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., & Deveci, M. (2023). A novel WENSLO and ALWAS multicriteria methodology and its application to green growth performance evaluation. IEEE Transactions on Engineering Management, 71, 9510-9525.
  23. Punetha, N., & Jain, G. (2023). Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews. Applied Intelligence, 53(17), 20152–20173. https://doi.org/10.1007/s10489-023-04471-1
  24. Punetha, N., & Jain, G. (2023). Unsupervised sentiment analysis of Hindi reviews using MCDM and game model optimization techniques. Sādhanā, 48(4), 195. https://doi.org/10.1007/s12046-023-02255-4
  25. Punetha, N., & Jain, G. (2025). Advancing sentiment analysis by addressing negation handling challenge via unsupervised mathematical approach. Social Network Analysis and Mining, 15(1), 20. https://doi.org/10.1007/s13278-025-01416-z
  26. Raffington, J., Steinke, D., & Tulpan, D. (2020). Recognition of arthropod species names using bigram-based classification. https://doi.org/10.21203/rs.3.rs-26532/v1
  27. Rocha, M., Santos, M., Fontes, R., Melo, A., Cunha-Oliveira, A., Miranda, A., … & Valentim, R. (2022). The text mining technique applied to the analysis of health interventions to combat congenital syphilis in brazil: the case of the “syphilis no!” project. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.855680
  28. Seth, A., James, A., Kuantama, E., Mukhopadhyay, S. C., & Han, R. (2023). Aerodynamics and sensing analysis for efficient drone-based parcel delivery. 2023 16th International Conference on Sensing Technology (ICST), 1–6. https://doi.org/10.1109/icst59744.2023.10460847
  29. Sharma, H., Tandon, A., Kapur, P. K., & Aggarwal, A. G. (2019). Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS. International Journal of System Assurance Engineering and Management, 10(5), 973–983. https://doi.org/10.1007/s13198-019-00827-4
  30. Tayal, D. K., Yadav, S. K., & Arora, D. (2023). Personalized ranking of products using aspect-based sentiment analysis and Plithogenic sets. Multimedia Tools and Applications, 82(1), 1261–1287. https://doi.org/10.1007/s11042-022-13315-y
  31. Vyas, V., Uma, V., & Ravi, K. (2022). Aspect-based approach to measure performance of financial services using voice of customer. Journal of King Saud University - Computer and Information Sciences, 34(5), 2262–2270. https://doi.org/10.1016/j.jksuci.2019.12.009
  32. Wang, L., Wang, X., Peng, J., & Wang, J. (2020). The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model. Tourism Management, 76, 103961. https://doi.org/10.1016/j.tourman.2019.103961
  33. Wang, Z., Liu, H., & Fan, X. (2025). Hybrid machine learning and MCDM framework for consumer preference extraction and decision support in dynamic markets. Technology in Society, 82, 102926. https://doi.org/10.1016/j.techsoc.2025.102926
  34. Wu, C., & Zhang, D. (2019). Ranking products with IF-based sentiment word framework and TODIM method. Kybernetes, 48(5), 990–1010. https://doi.org/10.1108/K-01-2018-0029
  35. Xiao, S., & Chen, X. (2025). Measuring social media customer engagement with brands based on information entropy: An application case of luxury brand. Journal of Brand Management, 32(3), 184–202. https://doi.org/10.1057/s41262-024-00376-7
  36. Yang, Z., Ouyang, T., Fu, X., & Peng, X. (2020). A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q‐rung orthopair fuzzy interaction Heronian mean operators. International Journal of Intelligent Systems, 35(5), 783–825. https://doi.org/10.1002/int.22225
  37. Yang, Z., Xiong, G., Cao, Z., Li, Y., & Huang, L. (2019). A decision method for online purchases considering dynamic information preference based on sentiment orientation classification and discrete DIFWA operators. IEEE Access, 7, 77008–77026. https://doi.org/10.1109/ACCESS.2019.2921403
  38. Zhang, C., Tian, Y., Fan, L., & Li, Y. (2020). Customized ranking for products through online reviews: A method incorporating prospect theory with an improved VIKOR. Applied Intelligence, 50(6), 1725–1744. https://doi.org/10.1007/s10489-019-01577-3
  39. Zhou, S., & Hudin, N. S. (2024). Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and graph neural network-enhanced user profiling. PLOS ONE, 19(4), e0299087. https://doi.org/10.1371/journal.pone.0299087