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Bir Müşteri için Türkiye’deki Popüler Online Yemek Sipariş Uygulamalarının Duygu Analizi ve ÇKKV Yöntemleri ile Sıralanması Skip to content Yıl 2024, Cilt: 16 Sayı: 2, 832 – 851, 30.06.2024
Sena Nur Fatma Kaya Nurhayat Bozdaş Gültekin Çağıl
https://doi.org/10.29137/umagd.1449759
Öz Online alışverişin hızla önem kazandığı günümüzde, kullanıcı yorumları hem müşteriler hem de firmalar açısından önem kazanmıştır. Kullanıcı yorumlarının her geçen gün artması herhangi bir kullanıcı yorumunun anlamsal ve duygusal çözümlemesine ihtiyaç duyulmasına sebep olmaktadır. Bu çalışmanın amacı, Türkiye’deki online yemek sipariş sektörü uygulamasını müşterilerinin hangi uygulamayı kullanacağına karar vermesini kolaylaştırmaktır. Literatürde bu amaca uygun, müşterilerin karar vermelerine yardımcı olacak bir çalışmaya rastlanmamıştır. Türkiye’de popüler olan İstegelsin, Glovo, Getir ve Yemeksepeti uygulamaları seçilerek kullanıcı yorumları Google Play platformundan elde edilmiştir. Kullanıcı yorumlarına; fiyat, hız, kurye, lezzet, adres ve arayüz olarak belirlenen değerlendirme kriterleri baz alınarak Duygu Analizi yapılmıştır. Duygu Analizi sonucunda bir karar matrisi oluşturulmuş ve bu matris Çok Kriterli Karar Verme (ÇKKV) için başlangıç matrisi olarak kullanılmıştır. Çalışmanın amacına uygun olarak ÇKKV yöntemleri ile yemek sipariş uygulamaları sıralanmış ve sonucunda bir müşterinin online yemek siparişi vermek istediğinde Getir uygulamasını ilk, İstegelsin uygulamasını ikinci, Glovo üçüncü, Yemeksepeti uygulamasını ise dördüncü sırada tercih ettiği/etmesi gerektiği sonucuna ulaşılmıştır.
Anahtar Kelimeler Online yemek sipariş sektörü, duygu analizi, entropi, çkkv
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An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making. Technological Forecasting and Social Change, 173, 121158. doi: 10.1016/j.techfore.2021.121158 Dönmez, İ., & Aslan, Z. (2021). Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(2), 701-714. doi.org/10.17341/gazimmfd.701313 ERCAN, E., & KUNDAKCI, N. (2017). Comparison of ARAS and OCRA Methods in the Selection of Pattern Software for a Textile Company. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 19(1), 83-105. doi:10.5578/jss.53866 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, 5247-5261. doi:10.1007/s00500-017-2961-4 Cobanoglu, C., Terrah, A., Hsu, M. J., Corte, V. D., & Gaudio, G. D. (2022). A systematic review of big data: research approaches and future prospects. Journal of Smart Tourism, 2(1), 21-31. doi:10.52255/smarttourism.2022.2.1.3 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. doi:10.1111/itor.12569 Ji, P., Zhang, H. Y., & Wang, J. Q. (2018). 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. doi:10.1109/TSMC.2018.2875163 Kılıçer, S., & Şamlı, R. (2023). E-Ticaret Sitelerindeki Türkçe Ürün Yorumları Üzerine Makine Öğrenmesi Algoritmaları ile Duygu Analizi. Veri Bilimi, 6(2), 15-23. Kumar, G. (2018, May). A multi-criteria decision making approach for recommending a product using sentiment analysis. In 2018 12th International Conference on Research Challenges in Information Science (RCIS) (pp. 1-6). IEEE. doi I:10.1109/RCIS.2018.8406679 Kumar, G., & Parimala, N. (2020). An integration of sentiment analysis and MCDM approach for smartphone recommendation. International Journal of Information Technology & Decision Making, 19(04), 1037-1063. doi:10.1142/S021962202050025X Liang, X., Liu, P., & Wang, Z. (2019). Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method. Technological and Economic Development of Economy, 25(6), 1139-1161. doi.org/10.3846/tede.2019.10766 Liang, R., & Wang, J. Q. (2019). A linguistic intuitionistic cloud decision support model with sentiment analysis for product selection in E-commerce. International Journal of Fuzzy Systems, 21, 963-977. doi:10.1007/s40815-019-00606-0 Liu, P., & Teng, F. (2019). Probabilistic linguistic TODIM method for selecting products through online product reviews. Information Sciences, 485, 441-455. doi: 10.1016/j.ins.2019.02.022 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. doi:10.1080/21681015.2022.2070933 Atan, M., & Altan, Ş. (2020). Örnek uygulamalarla çok kriterli karar verme yöntemleri. Gazi Kitabevi, Ankara. 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, 106935. doi: 10.1016/j.asoc.2020.106935 Scholz, M., Dorner, V., Schryen, G., & Benlian, A. (2017). A configuration-based recommender system for supporting e-commerce decisions. European Journal of Operational Research, 259(1), 205-215. doi: 10.1016/j.ejor.2016.09.057 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, 973-983. doi:10.1007/s13198-019-00827-4 Sun, J., Long, C., Zhu, X., & Huang, M. (2009). Mining reviews for product comparison and recommendation. Polibits, (39), 33-40. doi:10.17562/PB-39-5 Sun, L., Chen, G., Xiong, H., & Guo, C. (2017). Cluster analysis in data‐driven management and decisions. Journal of Management Science and Engineering, 2(4), 227-251. doi:10.3724/SP.J.1383.204011 Sliogeriene, J., Turskis, Z., & Streimikiene, D. (2013). Analysis and choice of energy generation technologies: The multiple criteria assessment on the case study of Lithuania. Energy Procedia, 32, 11-20. doi: 10.1016/j.egypro.2013.05.003 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. doi:10.1007/s11042-022-13315-y Tsai, C. F., Chen, K., Hu, Y. H., & Chen, W. K. (2020). Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Management, 80, 104122. doi: 10.1016/j.tourman.2020.104122 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. doi.org/10.1016/j.jksuci.2019.12.009 Yang, Z., Gao, Y., & Fu, X. (2021). A decision-making algorithm combining the aspect-based sentiment analysis and intuitionistic fuzzy-VIKOR for online hotel reservation. Annals of Operations Research, 1-17. doi:10.1007/s10479-021-04339-y 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. doi:10.1002/int.22225 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. doi:10.1109/ACCESS.2019.2921403 Yu, S. M., Wang, J., & Wang, J. Q. (2017). An interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website. International Journal of Fuzzy Systems, 19, 47-61. doi.org/10.1007/s40815-016-0217-6 Zaman, M., Botti, L., & Vo-Thanh, T. (2016). Weight of criteria in hotel selection: An empirical illustration based on TripAdvisor criteria. European journal of tourism research, 13, 132-138. doi:10.54055/ejtr. v13i.236 Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. doi: 10.5755/j01.eee.122.6.1810 Zavadskas, E. K., Antuchevičienė, J., Šaparauskas, J., & Turskis, Z. (2013). MCDM methods WASPAS and MULTIMOORA: Verification of robustness of methods when assessing alternative solutions. Zhang, D., Li, Y., & Wu, C. (2020). An extended TODIM method to rank products with online reviews under intuitionistic fuzzy environment. Journal of the Operational Research Society, 71(2), 322-334. doi.org/10.1080/01605682.2018.1545519 Zhang, C., Tian, Y. X., Fan, L. W., & Li, Y. H. (2020). Customized ranking for products through online reviews: a method incorporating prospect theory with an improved VIKOR. Applied Intelligence, 50, 1725-1744. doi:10.1007/s10489-019-01577-3 Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. The Scientific World Journal, 2014. doi:10.1155/2014/797814 Zhou, Y., Li, X., Wang, X., & Yuen, K. F. (2022). Intelligent container shipping sustainability disclosure via stakeholder sentiment views on social media. Marine Policy, 135, 104853. doi: 10.1016/j.marpol.2021.104853 Zuheros, C., Martínez-Cámara, E., Herrera-Viedma, E., & Herrera, F. (2021). Sentiment analysis based multi-person multi-criteria decision making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews. Information Fusion, 68, 22-36. doi: 10.1016/j.inffus.2020.10.019 Qin, Y., Wang, X., & Xu, Z. (2022). Ranking tourist attractions through online reviews: A novel method with intuitionistic and hesitant fuzzy information based on sentiment analysis. International journal of fuzzy systems, 24(2), 755-777. doi:10.1007/s40815-021-01131-9 Wang, L., Wang, X. K., Peng, J. J., & Wang, J. Q. (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. doi: 10.1016/j.tourman.2019.103961 Wu, C., & Zhang, D. (2019). Ranking products with IF-based sentiment word framework and TODIM method. Kybernetes, 48(5), 990-1010. doi:10.1108/K-01-2018-0029
Ranking of Popular Online Food Ordering Applications in Turkey for a Customer with Sentiment Analysis and MCDM Methods Yıl 2024, Cilt: 16 Sayı: 2, 832 – 851, 30.06.2024
Sena Nur Fatma Kaya Nurhayat Bozdaş Gültekin Çağıl
https://doi.org/10.29137/umagd.1449759
Öz In today’s world where online shopping is rapidly gaining importance, user comments have gained importance for both customers and companies. The increase in user comments every day causes the need for a semantic and emotional analysis of any user comment. The aim of this study is to make it easier for online food ordering industry customers in Turkey to decide which application to use. There is no study in the literature that is suitable for this purpose and that will help customers make decisions. The popular applications in Turkey İstegelsin, Glovo, Getir and Yemeksepeti were selected and user comments were obtained from the Google Play platform. User comments; Sentiment Analysis was performed based on the evaluation criteria determined as price, speed, courier, taste, address and interface. As a result of Sentiment Analysis, a decision matrix was created and this matrix was used as the starting matrix for Multi-Criteria Decision Making (MCDM). In accordance with the purpose of the study, food ordering applications with MCDM methods were listed and as a result, it was concluded that when a customer wants to order food online, he prefers the Getir application first, the İstegelsin application second, the Glovo application third, and the Yemeksepeti application in the fourth place.
Anahtar Kelimeler Online food ordering industry, sentiment analysis, entropy, mcdm
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An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making. Technological Forecasting and Social Change, 173, 121158. doi: 10.1016/j.techfore.2021.121158 Dönmez, İ., & Aslan, Z. (2021). Metin Duygu sınıflandırılmasında hibrit wavelet yönteminin kullanımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(2), 701-714. doi.org/10.17341/gazimmfd.701313 ERCAN, E., & KUNDAKCI, N. (2017). Comparison of ARAS and OCRA Methods in the Selection of Pattern Software for a Textile Company. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 19(1), 83-105. doi:10.5578/jss.53866 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, 5247-5261. doi:10.1007/s00500-017-2961-4 Cobanoglu, C., Terrah, A., Hsu, M. J., Corte, V. D., & Gaudio, G. D. (2022). A systematic review of big data: research approaches and future prospects. Journal of Smart Tourism, 2(1), 21-31. doi:10.52255/smarttourism.2022.2.1.3 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. doi:10.1111/itor.12569 Ji, P., Zhang, H. Y., & Wang, J. Q. (2018). 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. doi:10.1109/TSMC.2018.2875163 Kılıçer, S., & Şamlı, R. (2023). E-Ticaret Sitelerindeki Türkçe Ürün Yorumları Üzerine Makine Öğrenmesi Algoritmaları ile Duygu Analizi. Veri Bilimi, 6(2), 15-23. Kumar, G. (2018, May). A multi-criteria decision making approach for recommending a product using sentiment analysis. In 2018 12th International Conference on Research Challenges in Information Science (RCIS) (pp. 1-6). IEEE. doi I:10.1109/RCIS.2018.8406679 Kumar, G., & Parimala, N. (2020). An integration of sentiment analysis and MCDM approach for smartphone recommendation. International Journal of Information Technology & Decision Making, 19(04), 1037-1063. doi:10.1142/S021962202050025X Liang, X., Liu, P., & Wang, Z. (2019). Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method. Technological and Economic Development of Economy, 25(6), 1139-1161. doi.org/10.3846/tede.2019.10766 Liang, R., & Wang, J. Q. (2019). A linguistic intuitionistic cloud decision support model with sentiment analysis for product selection in E-commerce. International Journal of Fuzzy Systems, 21, 963-977. doi:10.1007/s40815-019-00606-0 Liu, P., & Teng, F. (2019). Probabilistic linguistic TODIM method for selecting products through online product reviews. Information Sciences, 485, 441-455. doi: 10.1016/j.ins.2019.02.022 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. doi:10.1080/21681015.2022.2070933 Atan, M., & Altan, Ş. (2020). Örnek uygulamalarla çok kriterli karar verme yöntemleri. Gazi Kitabevi, Ankara. 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, 106935. doi: 10.1016/j.asoc.2020.106935 Scholz, M., Dorner, V., Schryen, G., & Benlian, A. (2017). A configuration-based recommender system for supporting e-commerce decisions. European Journal of Operational Research, 259(1), 205-215. doi: 10.1016/j.ejor.2016.09.057 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, 973-983. doi:10.1007/s13198-019-00827-4 Sun, J., Long, C., Zhu, X., & Huang, M. (2009). Mining reviews for product comparison and recommendation. Polibits, (39), 33-40. doi:10.17562/PB-39-5 Sun, L., Chen, G., Xiong, H., & Guo, C. (2017). Cluster analysis in data‐driven management and decisions. Journal of Management Science and Engineering, 2(4), 227-251. doi:10.3724/SP.J.1383.204011 Sliogeriene, J., Turskis, Z., & Streimikiene, D. (2013). Analysis and choice of energy generation technologies: The multiple criteria assessment on the case study of Lithuania. Energy Procedia, 32, 11-20. doi: 10.1016/j.egypro.2013.05.003 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. doi:10.1007/s11042-022-13315-y Tsai, C. F., Chen, K., Hu, Y. H., & Chen, W. K. (2020). Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Management, 80, 104122. doi: 10.1016/j.tourman.2020.104122 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. doi.org/10.1016/j.jksuci.2019.12.009 Yang, Z., Gao, Y., & Fu, X. (2021). A decision-making algorithm combining the aspect-based sentiment analysis and intuitionistic fuzzy-VIKOR for online hotel reservation. Annals of Operations Research, 1-17. doi:10.1007/s10479-021-04339-y 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. doi:10.1002/int.22225 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. doi:10.1109/ACCESS.2019.2921403 Yu, S. M., Wang, J., & Wang, J. Q. (2017). An interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website. International Journal of Fuzzy Systems, 19, 47-61. doi.org/10.1007/s40815-016-0217-6 Zaman, M., Botti, L., & Vo-Thanh, T. (2016). Weight of criteria in hotel selection: An empirical illustration based on TripAdvisor criteria. European journal of tourism research, 13, 132-138. doi:10.54055/ejtr. v13i.236 Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. doi: 10.5755/j01.eee.122.6.1810 Zavadskas, E. K., Antuchevičienė, J., Šaparauskas, J., & Turskis, Z. (2013). MCDM methods WASPAS and MULTIMOORA: Verification of robustness of methods when assessing alternative solutions. Zhang, D., Li, Y., & Wu, C. (2020). An extended TODIM method to rank products with online reviews under intuitionistic fuzzy environment. Journal of the Operational Research Society, 71(2), 322-334. doi.org/10.1080/01605682.2018.1545519 Zhang, C., Tian, Y. X., Fan, L. W., & Li, Y. H. (2020). Customized ranking for products through online reviews: a method incorporating prospect theory with an improved VIKOR. Applied Intelligence, 50, 1725-1744. doi:10.1007/s10489-019-01577-3 Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. The Scientific World Journal, 2014. doi:10.1155/2014/797814 Zhou, Y., Li, X., Wang, X., & Yuen, K. F. (2022). Intelligent container shipping sustainability disclosure via stakeholder sentiment views on social media. Marine Policy, 135, 104853. doi: 10.1016/j.marpol.2021.104853 Zuheros, C., Martínez-Cámara, E., Herrera-Viedma, E., & Herrera, F. (2021). Sentiment analysis based multi-person multi-criteria decision making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews. Information Fusion, 68, 22-36. doi: 10.1016/j.inffus.2020.10.019 Qin, Y., Wang, X., & Xu, Z. (2022). Ranking tourist attractions through online reviews: A novel method with intuitionistic and hesitant fuzzy information based on sentiment analysis. International journal of fuzzy systems, 24(2), 755-777. doi:10.1007/s40815-021-01131-9 Wang, L., Wang, X. K., Peng, J. J., & Wang, J. Q. (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. doi: 10.1016/j.tourman.2019.103961 Wu, C., & Zhang, D. (2019). Ranking products with IF-based sentiment word framework and TODIM method. Kybernetes, 48(5), 990-1010. doi:10.1108/K-01-2018-0029 Toplam 47 adet kaynakça vardır.
Ayrıntılar Birincil Dil Türkçe Konular Çok Ölçütlü Karar Verme Bölüm Makaleler Yazarlar Sena Nur Fatma Kaya SAKARYA ÜNİVERSİTESİ 0000-0001-8810-8910 Türkiye
Nurhayat Bozdaş SAKARYA ÜNİVERSİTESİ 0000-0002-6543-5887 Türkiye
Gültekin Çağıl SAKARYA ÜNİVERSİTESİ 0000-0001-8609-6178 Türkiye
Erken Görünüm Tarihi 30 Haziran 2024 Yayımlanma Tarihi 30 Haziran 2024 Gönderilme Tarihi 9 Mart 2024 Kabul Tarihi 30 Mayıs 2024 Yayımlandığı Sayı Yıl 2024 Cilt: 16 Sayı: 2
Kaynak Göster APA Kaya, S. N. F., Bozdaş, N., & Çağıl, G. (2024). Bir Müşteri için Türkiye’deki Popüler Online Yemek Sipariş Uygulamalarının Duygu Analizi ve ÇKKV Yöntemleri ile Sıralanması. International Journal of Engineering Research and Development, 16(2), 832-851. https://doi.org/10.29137/umagd.1449759
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