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Bir Müşteri için Türkiye’deki Popüler Online Yemek Sipariş Uygulamalarının Duygu Analizi ve ÇKKV Yöntemleri ile Sıralanması

Yıl 2024, Cilt: 16 Sayı: 2, 833 - 852, 30.06.2024
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.

Kaynakça

  • Abirami, A. M., & Askarunisa, A. (2017). Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Information Review, 41(4), 471-486. doi:10.1108/OIR-08-2015-0289
  • Abo, M. E. M., Idris, N., Mahmud, R., Qazi, A., Hashem, I. A. T., Maitama, J. Z., ... & Yang, S. (2021). A multi-criteria approach for arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection. Sustainability, 13(18), 10018. doi:10.3390/su131810018
  • Alasmari, S. F., & Dahab, M. (2017). Sentiment detection, recognition and aspect identification. International Journal of Computer Applications, 975, 8887. doi:10.5120/ijca2017915675
  • Banaitiene, N., Banaitis, A., Kaklauskas, A., & Zavadskas, E. K. (2008). Evaluating the life cycle of a building: A multivariant and multiple criteria approach. Omega, 36(3), 429-441. doi: 10.1016/j.omega.2005.10.010
  • Bueno, I., Carrasco, R. A., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320. doi: 10.1016/j.ins.2021.12.080
  • Bueno, I., Carrasco, R. A., Porcel, C., Kou, G., & Herrera-Viedma, E. (2021). A linguistic multi-criteria decision making methodology for the evaluation of tourist services considering customer opinion value. Applied Soft Computing, 101, 107045. doi: 10.1016/J.ASOC.2020.107045
  • 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. doi: 10.1016/j.cie.2019.01.051
  • Dadelo, S., Turskis, Z., Zavadskas, E. K., & Dadelienė, R. (2012). Multiple criteria assessment of elite security personal on the basis of ARAS and expert methods.
  • Dahooie, J. H., 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. 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, 833 - 852, 30.06.2024
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.

Kaynakça

  • Abirami, A. M., & Askarunisa, A. (2017). Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Information Review, 41(4), 471-486. doi:10.1108/OIR-08-2015-0289
  • Abo, M. E. M., Idris, N., Mahmud, R., Qazi, A., Hashem, I. A. T., Maitama, J. Z., ... & Yang, S. (2021). A multi-criteria approach for arabic dialect sentiment analysis for online reviews: Exploiting optimal machine learning algorithm selection. Sustainability, 13(18), 10018. doi:10.3390/su131810018
  • Alasmari, S. F., & Dahab, M. (2017). Sentiment detection, recognition and aspect identification. International Journal of Computer Applications, 975, 8887. doi:10.5120/ijca2017915675
  • Banaitiene, N., Banaitis, A., Kaklauskas, A., & Zavadskas, E. K. (2008). Evaluating the life cycle of a building: A multivariant and multiple criteria approach. Omega, 36(3), 429-441. doi: 10.1016/j.omega.2005.10.010
  • Bueno, I., Carrasco, R. A., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320. doi: 10.1016/j.ins.2021.12.080
  • Bueno, I., Carrasco, R. A., Porcel, C., Kou, G., & Herrera-Viedma, E. (2021). A linguistic multi-criteria decision making methodology for the evaluation of tourist services considering customer opinion value. Applied Soft Computing, 101, 107045. doi: 10.1016/J.ASOC.2020.107045
  • 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. doi: 10.1016/j.cie.2019.01.051
  • Dadelo, S., Turskis, Z., Zavadskas, E. K., & Dadelienė, R. (2012). Multiple criteria assessment of elite security personal on the basis of ARAS and expert methods.
  • Dahooie, J. H., 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. 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 0000-0001-8810-8910

Nurhayat Bozdaş 0000-0002-6543-5887

Gültekin Çağıl 0000-0001-8609-6178

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), 833-852. https://doi.org/10.29137/umagd.1449759
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.