Araştırma Makalesi
BibTex RIS Kaynak Göster

Pazarlama stratejisinde önemli bir parametre olarak tüketici yorumları: tüketici yorumlarındaki puanlamalar ile duygusal eğilimler arasındaki ilişki

Yıl 2022, Cilt: 15 Sayı: 2, 470 - 488, 31.12.2022

Hıdır Polat Yılmaz Ağca

https://doi.org/10.17218/hititsbd.1127965

Öz

Sosyal medya insanların duygularını yaşadıkları ve paylaştıkları bir alana dönüşmüştür. Dolayısıyla bireylerin satın aldıkları ürün ya da hizmetlerle alakalı yaptıkları yorumlar ve değerlendirme puanlamaları, diğer müşterilerin satın alma davranışlarını etkilemektedir. Müşteriler, kullanıcıların duygusal eğilimlerine ilişkin kanıya genellikle verdikleri puanlamalar üzerinden ulaşmaktadır. İşletmelerin ise, kullanıcı yorumlarında saklı olan duygusal eğilimleri kullanıcı puanlamaları üzerinden tahmin etmeleri pazarlama sürecindeki atılacak adımları sorgulanabilir kılmaktadır. Bazen tüketiciler bir ürüne verdiği düşük puanlı bir yorumda olumlu ifadeleri çok daha fazla kullanabilmekte ve düşük puanın gerekçesini tek bir faktöre bağlayabilmektedir. Buna benzer örnekler, puanlar ile yorumlar arasındaki ilişkinin sorgulanmasına yol açmaktadır. Araştırmanın amacı, tüketicilerin ürün ve hizmet kullanımından sonra verdikleri puanların, yorumlardaki duygusal eğilimlerin bir ölçüsü olarak kabul edilip edilemeyeceğini sorgulamaktır. Kullanıcı yorumlarına yönelik gerçekleştirilen metin madenciliği uygulaması sebebiyle araştırma nicel araştırma özelliğine sahiptir. Verilerin toplanması sürecinde web madenciliği/kazıma tekniği kullanılmıştır. Veriler popüler turizm platformu olan TripAdvisor.com üzerinden elde edilmiştir. Elde edilen verilerin analiz edilmesinde metin madenciliği tekniklerinden biri olan duygu analizi kullanılmıştır. Verilerin analiz sürecinde ise veri madenciliğinde etkin kullanıma sahip olan R programlama dilinden yararlanılmıştır. Araştırma neticesinde, tüketici puanlamalarının pozitif duygusal eğilimleri yansıtma başarısının daha yüksek olduğu; negatif duygusal eğilimlerle arasında açıklık olduğu görülmektedir.

Anahtar Kelimeler

Pazarlama tüketici yorumları tüketici puanlamaları duygu analizi

Destekleyen Kurum

Yok

Proje Numarası

Yok

Kaynakça

  • Akter, S. ve Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda. Electron Markets, 173-194. doi:10.1007/s12525-016-0219-0
  • Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28–44. doi:10.1016/J.IJINFOMGT.2019.04.008
  • Alzate, M., Arce-Urriza, M. ve Cebollada, J. (2022). Mining the text of online consumer reviews to analyze brand image and brand positioning. Journal of Retailing and Consumer Services, 67, 1-29. 102989. doi:10.1016/J.JRETCONSER.2022.102989
  • Antonio, N., De Almeida, A., Nunes, · L., Fernando B., Ribeiro, R., Nunes, L. ve Batista, F. (2018). Hotel online reviews: different languages, different opinions. Information Technology & Tourism, 18, 157–185. doi:10.1007/s40558-018-0107-x
  • Arai, K., Sakurai, Y., Sakurai, E., Tsuruta, S. ve Knauf, R. (2019). Visualization system for analyzing customer comments in marketing research support system. 2019 IEEE World Congress on Services (SERVICES). Milan, Italy : IEEE. doi: 10.1109/SERVICES.2019.00042.
  • Basiri, M. E., Nemati, S., Abdar, M., Asadi, S. ve Acharrya, U. R. (2021). A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 1-21. doi:10.1016/j.knosys.2021.107242
  • Brownlee, J. (2019). What is natural language processing? Erişim Adresi: https://machinelearningmastery.com/natural-language-processing/
  • Chatterjee, S. (2019). Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents. Decision Support Systems, 119, 14–22. doi:10.1016/J.DSS.2019.02.008
  • Chevalier, J. A. ve Mayzlin, D. (2018). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354. doi:10.1509/JMKR.43.3.345
  • Chevalier, J. ve Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quantitative Marketing and Economics, 1(2), 203–222. doi:10.1023/A:1024634613982
  • Choe, P., Lehto, M. R., Shin, G.-C. ve Choi, K.-Y. (2012). Semiautomated identification and classification of customer complaints. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(2), 149-162. doi:10.1002/hfm.20325
  • Coppola, D. (2022). E-commerce worldwide. Erişim Adresi: https://www.statista.com/topics/871/online-shopping/
  • Coulter, K. S. ve Roggeveen, A. (2012). “Like it or not”: Consumer responses to word-of-mouth communication in on-line social networks. Management Research Review, 35(9), 878–899. doi:10.1108/01409171211256587/FULL/PDF
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B. ve Oflazer, K. (2016). SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50, 667–685. doi:10.1007/s10579-015-9307-6
  • Deng, L. ve Liu, Y. (2018). Deep Learning in Natural Language Processing. Singapore: Springer Nature Singapore.
  • Deng, S., Sinha, A. P. ve Zhao, H. (2017). Adapting sentiment lexicons to domain-specific social media texts. Decision Support Systems, 94, 65–76. doi:10.1016/J.DSS.2016.11.001
  • Dhar, S. ve Bose, I. (2022). Walking on air or hopping mad? Understanding the impact of emotions, sentiments and reactions on ratings in online customer reviews of mobile apps. Decision Support Systems, 1-12. doi:10.1016/J.DSS.2022.113769
  • Duan, W., Yu, Y., Cao, Q. ve Levy, S. (2015). Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly, 57(3), 282–296. doi:10.1177/1938965515620483
  • Estay, B. (2022). Fast, flexible, cost-effective e-commerce. Erişim Adresi: https://www.bigcommerce.com/blog/online-shopping-statistics/#5-essential-online-shopping-statistics
  • Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261–1270. doi:10.1016/J.JBUSRES.2014.11.006
  • Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y. ve Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232. doi:10.1016/J.JRETAI.2014.04.004
  • Fu, J. R., Ju, P. H. ve Hsu, C. W. (2015). Understanding why consumers engage in electronic word-of-mouth communication: Perspectives from theory of planned behavior and justice theory. Electronic Commerce Research and Applications, 14(6), 616–630. doi:10.1016/J.ELERAP.2015.09.003
  • Gaikwad, S. V., Chaugule, A. ve Patil, P. (2014). Text mining methods and techniques. International Journal of Computer Applications, 85(17), 42-45. doi:10.5120/14937-3507
  • Ghimire, B., Shanaev, S. ve Lin, Z. (2022). Effects of official versus online review ratings. Annals of Tourism Research, 92, 1-8. doi:10.1016/J.ANNALS.2021.10324
  • Godes, D. ve Mayzlin, D. (2004). Using Online conversations to study word-of-mouth communication. 23(4), 545-560. doi:10.1287/MKSC.1040.0071
  • Grashuis, J., Skevas, T. ve Segovia, M. S. (2020). Grocery shopping preferences during the COVID-19 pandemic. Sustainability 2020, 1-10. doi:10.3390/su12135369
  • Hagiwara, M. (2021). Real-World natural language processing. Shelter Island, NY, US: Manning Publications.
  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G. ve Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52. doi:10.1002/DIR.10073
  • Hu, N., Liu, L., Jie, A. E. ve Zhang, J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management volume, 9, 201–2014. doi:10.1007/s10799-008-0041-
  • Jain, M. (2020). Sentiment refinement by extraction of hidden ınformation from customer comments (Yayınlanmamış Doktora Tezi). Delhi, India: Delhi Technological University.
  • Kang, Z. (2017). Sentiment analysis system on automobile customer comments. 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (s. 42-46). Advances in Engineering Research, Atlantis Press. doi:10.2991/icmmcce-17.2017.10
  • Kaur, H., Ahsaan, S. U., Alankar, B. ve Chang, V. (2021). A Proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Information Systems Frontiers, 23, 1417-1429. doi:10.1007/s10796-021-10135-7
  • Kim, W. G., Lim, H. ve Brymer, R. A. (2015). The effectiveness of managing social media on hotel performance. International Journal of Hospitality Management, 44, 165–171. doi:10.1016/J.IJHM.2014.10.01
  • Kim, Y. A. ve Srivastava, J. (2007). Impact of social influence in e-commerce decision making. ICEC '07: Proceedings of the ninth international conference on Electronic commerce, (s. 293-302). doi:10.1145/1282100.1282157
  • Lee, J.Y., Choi, J.W., Choi, J. ve Lee B. (202). Text-mining analysis using national R&D project data of South Korea to investigate innovation in graphene environment technology. International Journal Innovation Studies, 7(1), 87-99. doi:10.1016/j.ijis.2022.09.005
  • Li, R., Chen, H., Feng, F., Ma, Z., Wang, X. ve Hovy, E. (2021). Dual graph convolutional networks for aspect-based sentiment analysis. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (s. 6319–6329). Online: ACL Anthology. Erişim Adresi: https://aclanthology.org/2021.acl-long.494.pdf
  • Li, S., Lee-Won, R. J. ve McKnight, J. (2018). Effects of online physician reviews and physician gender on perceptions of physician skills and primary care physician (PCP) selection. 34(11), 1250–1258. doi:10.1080/10410236.2018.1475192
  • Li, X., Liu, H. ve Zhu, B. (2020). Evolutive preference analysis with online consumer ratings. Information Sciences, 541, 332–344. doi:10.1016/J.INS.2020.06.048
  • Liu, Y., Wan, Y., Shen, X., Ye, Z. ve Wen, J. (2021). Product customer satisfaction measurement based on multiple online consumer review features. Information, 12(6), 1-16. doi: doi:10.3390/info12060234
  • Palese, B. ve Usai, A. (2018). The relative importance of service quality dimensions in E-commerce experiences. International Journal of Information Management, 40, 132-140. doi:10.1016/j.ijinfomgt.2018.02.001
  • Pan, Y. ve Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612. doi:10.1016/J.JRETAI.2011.05.002
  • Pantelidis, I. S. (2010). Electronic meal experience: A content analysis of online restaurant comments. Cornell Hospitality Quarterly, 51(4), 483-491. doi:10.1177/1938965510378574
  • Park, S. ve Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83. doi:10.1016/J.ANNALS.2014.10.007
  • Piris, Y. ve Gay, A.-C. (2021). Customer satisfaction and natural language processing. Journal of Business Research, 264-271. doi:10.1016/j.jbusres.2020.11.065
  • Rai, A. (2019, 06 01). What is text mining: Techniques and applications. Erişim adresi: https://www.upgrad.com/blog/what-is-text-mining-techniques-and-applications/
  • Ramachandran, R., Sudhir, S. ve Unnithan, A. B. (2021). Exploring the relationship between emotionality and product star ratings in online reviews. IIMB Management Review, 33(4), 299–308. doi:10.1016/J.IIMB.2021.12.002
  • Rumelli, M., Akkuş, D., Kart, Ö. ve Isik, Z. (2019). Sentiment analysis in Turkish text with machine learning algorithms. 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5. doi:10.1109/ASYU48272.2019.8946436
  • Salehan, M. ve Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40. doi:10.1016/J.DSS.2015.10.006
  • Santos, X. M. ve Lopez, L. (2017). The location of tourist accommodation in Santiago de Compostela from a client perspective. e-Review of Tourism Research (eRTR), 14(5–6), 258–277. Erişim Adresi: https://journals.tdl.org/ertr/index.php/ertr/article/view/144/40
  • SenticNet. (t.y.). SenticNet. Erişim Adresi: https://sentic.net/
  • Serra Cantallops, A. ve Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, 36, 41–51. doi:10.1016/J.IJHM.2013.08.007
  • Shi, Y. ve Peng, Q. (2021). Enhanced customer requirement classification for product design using big data and improved Kano model. Advanced Engineering Informatics, 49, 1-12. doi: doi:10.1016/j.aei.2021.101340
  • Singh, M., Jakhar, A. K. ve Pandey, S. (2021). Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 1-11. doi: doi:10.1007/s13278-021-00737-z
  • Singh, P. K., Sachdeva, A., Mahajan, D., Pande, N. ve Sharma, A. (2014). An approach towards feature specific opinion mining and sentimental analysis across e-commerce websites. 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). Noida, India : IEEE. doi:10.1109/CONFLUENCE.2014.6949312
  • Tafesse, W. (2021). The effect of app store strategy on app rating: The moderating role of hedonic and utilitarian mobile apps. International Journal of Information Management, 57, 1-11. doi:10.1016/J.IJINFOMGT.2020.102299
  • Tian, G., Lu, L. ve McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 1-9. doi:10.1016/J.FOODQUAL.2020.104060
  • Tontini, G., Irgang, L., Kroenke, A., Hadlich, I., Picolo, J. D. ve Mikulic, J. (2021). How to use spontaneous customer comments to identify nonlinear background of satisfaction with restaurant services. Benchmarking: An International Journal, 29(2), 496-521. doi:0.1108/BIJ-08-2020-0409
  • Truyens, M. ve Eecke, P. V. (2014). Legal aspects of text mining. Comput. Law Security Review, 30(2),153-170. doi:10.1016/j.clsr.2014.01.009
  • Tsang, A. S. L. ve Prendergast, G. (2009). Does culture affect evaluation expressions?: A cross-cultural analysis of Chinese and American computer game reviews. European Journal of Marketing, 43(5–6), 686–707. doi:10.1108/03090560910947007/FULL/PDF
  • Tsao, W., Hsieh, M., Shih, L. ve Lin, T.M. (2015). Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity. International Journal of Hospitality Management, 46, 99-111. doi:10.1016/j.ijhm.2015.01.008
  • Tyagi, N. (2021). Top 7 text mining techniques. Erişim Adresi: https://www.analyticssteps.com/blogs/top-7-text-mining-techniques
  • Wang, F., Liu, X. ve Fang, E. (2015). User reviews variance, critic reviews variance, and product sales: An exploration of customer breadth and depth effects. Journal of Retailing, 91(3), 372–389. doi:10.1016/J.JRETAI.2015.04.00
  • Wei, Q., Shi, X., Li, Q. ve Chen, G. (2020). Enhancing customer satisfaction analysis with a machine learning approach: From a perspective of matching customer comment and agent note. Hawaii International Conference on System Sciences 2020 (HICSS-53). Grand Wailea, Hawaii. doi:10.24251/HICSS.2020.178
  • Wolff, R. (2020). What is text mining with sentiment analysis? Erişim Adresi: https://monkeylearn.com/blog/text-mining-sentiment-analysis/
  • Yi, J. ve Oh, Y. K. (2022). The informational value of multi-attribute online consumer reviews: A text mining approach. Journal of Retailing and Consumer Services, 65, 1-7. doi:10.1016/J.JRETCONSER.2021.102519

Consumer Comments as an Important Parameter in Marketing Strategy: The Relationship Between the Scorings in Consumer Comments and Emotional Trends

Yıl 2022, Cilt: 15 Sayı: 2, 470 - 488, 31.12.2022

Hıdır Polat Yılmaz Ağca

https://doi.org/10.17218/hititsbd.1127965

Öz

Social media has become an area where people live and share their emotions. Therefore, the comments and evaluation ratings individuals make about the products or services they purchase affect the purchasing behavior of other customers. Customers generally reach an opinion about the emotional tendencies of users through the ratings they give. The fact that businesses predict the emotional tendencies hidden in user comments through user ratings makes the steps to be taken in the marketing process questionable. Sometimes consumers may use positive expressions much more in a low-scoring review of a product and attribute the reason for a low score to a single factor. Similar examples lead to questioning the relationship between ratings and reviews. The research aim of the research is to investigate whether consumers' scores after product and service use can be considered as a measure of emotional tendencies in comments. The research has a quantitative characteristic due to the text mining application for user reviews. Web mining/scraping technique used in the data collection process. The data was obtained from TripAdvisor.com, a popular tourism platform. Sentiment analysis, one of the text mining techniques, was used to analyze the obtained data. R programming language, which has practical use in data mining, was used in the data analysis process. As a result of the research, it was observed that the success of consumer ratings in reflecting positive emotional tendencies is higher. At the same time, there is a gap between negative emotional tendencies.

Anahtar Kelimeler

Marketing consumer reviews consumer ratings sentiment analysis

Proje Numarası

Yok

Kaynakça

  • Akter, S. ve Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda. Electron Markets, 173-194. doi:10.1007/s12525-016-0219-0
  • Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28–44. doi:10.1016/J.IJINFOMGT.2019.04.008
  • Alzate, M., Arce-Urriza, M. ve Cebollada, J. (2022). Mining the text of online consumer reviews to analyze brand image and brand positioning. Journal of Retailing and Consumer Services, 67, 1-29. 102989. doi:10.1016/J.JRETCONSER.2022.102989
  • Antonio, N., De Almeida, A., Nunes, · L., Fernando B., Ribeiro, R., Nunes, L. ve Batista, F. (2018). Hotel online reviews: different languages, different opinions. Information Technology & Tourism, 18, 157–185. doi:10.1007/s40558-018-0107-x
  • Arai, K., Sakurai, Y., Sakurai, E., Tsuruta, S. ve Knauf, R. (2019). Visualization system for analyzing customer comments in marketing research support system. 2019 IEEE World Congress on Services (SERVICES). Milan, Italy : IEEE. doi: 10.1109/SERVICES.2019.00042.
  • Basiri, M. E., Nemati, S., Abdar, M., Asadi, S. ve Acharrya, U. R. (2021). A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 1-21. doi:10.1016/j.knosys.2021.107242
  • Brownlee, J. (2019). What is natural language processing? Erişim Adresi: https://machinelearningmastery.com/natural-language-processing/
  • Chatterjee, S. (2019). Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents. Decision Support Systems, 119, 14–22. doi:10.1016/J.DSS.2019.02.008
  • Chevalier, J. A. ve Mayzlin, D. (2018). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354. doi:10.1509/JMKR.43.3.345
  • Chevalier, J. ve Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quantitative Marketing and Economics, 1(2), 203–222. doi:10.1023/A:1024634613982
  • Choe, P., Lehto, M. R., Shin, G.-C. ve Choi, K.-Y. (2012). Semiautomated identification and classification of customer complaints. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(2), 149-162. doi:10.1002/hfm.20325
  • Coppola, D. (2022). E-commerce worldwide. Erişim Adresi: https://www.statista.com/topics/871/online-shopping/
  • Coulter, K. S. ve Roggeveen, A. (2012). “Like it or not”: Consumer responses to word-of-mouth communication in on-line social networks. Management Research Review, 35(9), 878–899. doi:10.1108/01409171211256587/FULL/PDF
  • Dehkharghani, R., Saygin, Y., Yanikoglu, B. ve Oflazer, K. (2016). SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50, 667–685. doi:10.1007/s10579-015-9307-6
  • Deng, L. ve Liu, Y. (2018). Deep Learning in Natural Language Processing. Singapore: Springer Nature Singapore.
  • Deng, S., Sinha, A. P. ve Zhao, H. (2017). Adapting sentiment lexicons to domain-specific social media texts. Decision Support Systems, 94, 65–76. doi:10.1016/J.DSS.2016.11.001
  • Dhar, S. ve Bose, I. (2022). Walking on air or hopping mad? Understanding the impact of emotions, sentiments and reactions on ratings in online customer reviews of mobile apps. Decision Support Systems, 1-12. doi:10.1016/J.DSS.2022.113769
  • Duan, W., Yu, Y., Cao, Q. ve Levy, S. (2015). Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly, 57(3), 282–296. doi:10.1177/1938965515620483
  • Estay, B. (2022). Fast, flexible, cost-effective e-commerce. Erişim Adresi: https://www.bigcommerce.com/blog/online-shopping-statistics/#5-essential-online-shopping-statistics
  • Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261–1270. doi:10.1016/J.JBUSRES.2014.11.006
  • Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y. ve Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232. doi:10.1016/J.JRETAI.2014.04.004
  • Fu, J. R., Ju, P. H. ve Hsu, C. W. (2015). Understanding why consumers engage in electronic word-of-mouth communication: Perspectives from theory of planned behavior and justice theory. Electronic Commerce Research and Applications, 14(6), 616–630. doi:10.1016/J.ELERAP.2015.09.003
  • Gaikwad, S. V., Chaugule, A. ve Patil, P. (2014). Text mining methods and techniques. International Journal of Computer Applications, 85(17), 42-45. doi:10.5120/14937-3507
  • Ghimire, B., Shanaev, S. ve Lin, Z. (2022). Effects of official versus online review ratings. Annals of Tourism Research, 92, 1-8. doi:10.1016/J.ANNALS.2021.10324
  • Godes, D. ve Mayzlin, D. (2004). Using Online conversations to study word-of-mouth communication. 23(4), 545-560. doi:10.1287/MKSC.1040.0071
  • Grashuis, J., Skevas, T. ve Segovia, M. S. (2020). Grocery shopping preferences during the COVID-19 pandemic. Sustainability 2020, 1-10. doi:10.3390/su12135369
  • Hagiwara, M. (2021). Real-World natural language processing. Shelter Island, NY, US: Manning Publications.
  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G. ve Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52. doi:10.1002/DIR.10073
  • Hu, N., Liu, L., Jie, A. E. ve Zhang, J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management volume, 9, 201–2014. doi:10.1007/s10799-008-0041-
  • Jain, M. (2020). Sentiment refinement by extraction of hidden ınformation from customer comments (Yayınlanmamış Doktora Tezi). Delhi, India: Delhi Technological University.
  • Kang, Z. (2017). Sentiment analysis system on automobile customer comments. 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (s. 42-46). Advances in Engineering Research, Atlantis Press. doi:10.2991/icmmcce-17.2017.10
  • Kaur, H., Ahsaan, S. U., Alankar, B. ve Chang, V. (2021). A Proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Information Systems Frontiers, 23, 1417-1429. doi:10.1007/s10796-021-10135-7
  • Kim, W. G., Lim, H. ve Brymer, R. A. (2015). The effectiveness of managing social media on hotel performance. International Journal of Hospitality Management, 44, 165–171. doi:10.1016/J.IJHM.2014.10.01
  • Kim, Y. A. ve Srivastava, J. (2007). Impact of social influence in e-commerce decision making. ICEC '07: Proceedings of the ninth international conference on Electronic commerce, (s. 293-302). doi:10.1145/1282100.1282157
  • Lee, J.Y., Choi, J.W., Choi, J. ve Lee B. (202). Text-mining analysis using national R&D project data of South Korea to investigate innovation in graphene environment technology. International Journal Innovation Studies, 7(1), 87-99. doi:10.1016/j.ijis.2022.09.005
  • Li, R., Chen, H., Feng, F., Ma, Z., Wang, X. ve Hovy, E. (2021). Dual graph convolutional networks for aspect-based sentiment analysis. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (s. 6319–6329). Online: ACL Anthology. Erişim Adresi: https://aclanthology.org/2021.acl-long.494.pdf
  • Li, S., Lee-Won, R. J. ve McKnight, J. (2018). Effects of online physician reviews and physician gender on perceptions of physician skills and primary care physician (PCP) selection. 34(11), 1250–1258. doi:10.1080/10410236.2018.1475192
  • Li, X., Liu, H. ve Zhu, B. (2020). Evolutive preference analysis with online consumer ratings. Information Sciences, 541, 332–344. doi:10.1016/J.INS.2020.06.048
  • Liu, Y., Wan, Y., Shen, X., Ye, Z. ve Wen, J. (2021). Product customer satisfaction measurement based on multiple online consumer review features. Information, 12(6), 1-16. doi: doi:10.3390/info12060234
  • Palese, B. ve Usai, A. (2018). The relative importance of service quality dimensions in E-commerce experiences. International Journal of Information Management, 40, 132-140. doi:10.1016/j.ijinfomgt.2018.02.001
  • Pan, Y. ve Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612. doi:10.1016/J.JRETAI.2011.05.002
  • Pantelidis, I. S. (2010). Electronic meal experience: A content analysis of online restaurant comments. Cornell Hospitality Quarterly, 51(4), 483-491. doi:10.1177/1938965510378574
  • Park, S. ve Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83. doi:10.1016/J.ANNALS.2014.10.007
  • Piris, Y. ve Gay, A.-C. (2021). Customer satisfaction and natural language processing. Journal of Business Research, 264-271. doi:10.1016/j.jbusres.2020.11.065
  • Rai, A. (2019, 06 01). What is text mining: Techniques and applications. Erişim adresi: https://www.upgrad.com/blog/what-is-text-mining-techniques-and-applications/
  • Ramachandran, R., Sudhir, S. ve Unnithan, A. B. (2021). Exploring the relationship between emotionality and product star ratings in online reviews. IIMB Management Review, 33(4), 299–308. doi:10.1016/J.IIMB.2021.12.002
  • Rumelli, M., Akkuş, D., Kart, Ö. ve Isik, Z. (2019). Sentiment analysis in Turkish text with machine learning algorithms. 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5. doi:10.1109/ASYU48272.2019.8946436
  • Salehan, M. ve Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40. doi:10.1016/J.DSS.2015.10.006
  • Santos, X. M. ve Lopez, L. (2017). The location of tourist accommodation in Santiago de Compostela from a client perspective. e-Review of Tourism Research (eRTR), 14(5–6), 258–277. Erişim Adresi: https://journals.tdl.org/ertr/index.php/ertr/article/view/144/40
  • SenticNet. (t.y.). SenticNet. Erişim Adresi: https://sentic.net/
  • Serra Cantallops, A. ve Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, 36, 41–51. doi:10.1016/J.IJHM.2013.08.007
  • Shi, Y. ve Peng, Q. (2021). Enhanced customer requirement classification for product design using big data and improved Kano model. Advanced Engineering Informatics, 49, 1-12. doi: doi:10.1016/j.aei.2021.101340
  • Singh, M., Jakhar, A. K. ve Pandey, S. (2021). Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 1-11. doi: doi:10.1007/s13278-021-00737-z
  • Singh, P. K., Sachdeva, A., Mahajan, D., Pande, N. ve Sharma, A. (2014). An approach towards feature specific opinion mining and sentimental analysis across e-commerce websites. 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). Noida, India : IEEE. doi:10.1109/CONFLUENCE.2014.6949312
  • Tafesse, W. (2021). The effect of app store strategy on app rating: The moderating role of hedonic and utilitarian mobile apps. International Journal of Information Management, 57, 1-11. doi:10.1016/J.IJINFOMGT.2020.102299
  • Tian, G., Lu, L. ve McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 1-9. doi:10.1016/J.FOODQUAL.2020.104060
  • Tontini, G., Irgang, L., Kroenke, A., Hadlich, I., Picolo, J. D. ve Mikulic, J. (2021). How to use spontaneous customer comments to identify nonlinear background of satisfaction with restaurant services. Benchmarking: An International Journal, 29(2), 496-521. doi:0.1108/BIJ-08-2020-0409
  • Truyens, M. ve Eecke, P. V. (2014). Legal aspects of text mining. Comput. Law Security Review, 30(2),153-170. doi:10.1016/j.clsr.2014.01.009
  • Tsang, A. S. L. ve Prendergast, G. (2009). Does culture affect evaluation expressions?: A cross-cultural analysis of Chinese and American computer game reviews. European Journal of Marketing, 43(5–6), 686–707. doi:10.1108/03090560910947007/FULL/PDF
  • Tsao, W., Hsieh, M., Shih, L. ve Lin, T.M. (2015). Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity. International Journal of Hospitality Management, 46, 99-111. doi:10.1016/j.ijhm.2015.01.008
  • Tyagi, N. (2021). Top 7 text mining techniques. Erişim Adresi: https://www.analyticssteps.com/blogs/top-7-text-mining-techniques
  • Wang, F., Liu, X. ve Fang, E. (2015). User reviews variance, critic reviews variance, and product sales: An exploration of customer breadth and depth effects. Journal of Retailing, 91(3), 372–389. doi:10.1016/J.JRETAI.2015.04.00
  • Wei, Q., Shi, X., Li, Q. ve Chen, G. (2020). Enhancing customer satisfaction analysis with a machine learning approach: From a perspective of matching customer comment and agent note. Hawaii International Conference on System Sciences 2020 (HICSS-53). Grand Wailea, Hawaii. doi:10.24251/HICSS.2020.178
  • Wolff, R. (2020). What is text mining with sentiment analysis? Erişim Adresi: https://monkeylearn.com/blog/text-mining-sentiment-analysis/
  • Yi, J. ve Oh, Y. K. (2022). The informational value of multi-attribute online consumer reviews: A text mining approach. Journal of Retailing and Consumer Services, 65, 1-7. doi:10.1016/J.JRETCONSER.2021.102519
Toplam 65 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Hıdır Polat 0000-0002-7839-4666

Yılmaz Ağca 0000-0002-5912-0977

Proje Numarası Yok
Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 8 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 15 Sayı: 2

Kaynak Göster

APA Polat, H., & Ağca, Y. (2022). Pazarlama stratejisinde önemli bir parametre olarak tüketici yorumları: tüketici yorumlarındaki puanlamalar ile duygusal eğilimler arasındaki ilişki. Hitit Sosyal Bilimler Dergisi, 15(2), 470-488. https://doi.org/10.17218/hititsbd.1127965
 Kapak Resmi İndir
                                                     Hitit Sosyal Bilimler Dergisi  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.