Hizmet kalitesi bağlamında duygu analizi kullanımına yönelik bibliyometrik analiz

Yıl 2023, Cilt: 8 Sayı: 1, 81 - 104, 20.03.2023

Erhan Sur , Hüseyin Çakır

https://doi.org/10.57120/yalvac.1258627

Cited By: 1

Öz

Duygu analizi çalışmalarının günden güne arttığı gözlenmektedir. Duygu analizi tekniği bilgisayar bilimleri içerisinde doğmuştur, tekniğinin farklı alanlarda kullanılabilir olmasıyla araştırmacılar farklı disiplinlerde bu tekniği kullanmaya başlamışlardır. İşletmeler, rekabetin artmasıyla birlikte müşterilerini memnun edebilmek için hizmet kalitesini arttırması gerektiğini anlamışlardır. İnsan davranışlarının dijitalleşmesiyle oluşan verinin duygu analiziyle işlenebilmesi, işletmeler açısından hizmet kalitesinin tespitinde önemli bir unsur haline gelmiştir. Bu çalışmada hizmet kalitesi bağlamında duygu analizinin literatürdeki yerinin belirlenebilmesi için bibliyometrik analizler yapılmıştır. Çalışmanın amacına ulaşabilmesi için öncelikle duygu analiziyle ilgili çalışmalarının bibliyometrik analizi yapılmıştır. Daha sonra ise duygu analizinin hizmet kalitesiyle kullanımına yönelik bibliyometrik analiz yapılarak duygu analizi tekniğinin hizmet kalitesi bağlamında literatür değerlendirilmesi yapılmıştır. Bibliyometrik analizlerin yanı sıra duygu analizi ve hizmet kalitesi bağlamında duygu analizi çalışmalarının başlık ve özetleri çalışma konularını ve tekniklerini belirleyebilmek amacıyla içerik analiziyle incelenmiştir. Hizmet kalitesi alanında duygu analizi tekniğinin kullanımının 2016 yılında başladığı ve bu alanda yapılan çalışmaların henüz az olduğu ve belirli sektörlerde kullanıldığı dikkat çekmiştir.

Anahtar Kelimeler

Duygu Analizi, Hizmet Kalitesi, Bibliyometrik, İçerik Analizi

Teşekkür

İlgilendiğiniz için teşekkür ederim.

Bibliometric analysis of the use of sentiment analysis in the context of service quality

Öz

The use of sentiment analysis has been increasing over time. Sentiment analysis was born in computer science, but researchers have begun to use this technique in different disciplines as it can be used in various fields. As competition increases, businesses have understood that they need to improve the quality of their services to satisfy their customers. The processing of human behavior data with sentiment analysis has become an important factor in determining the quality of service for businesses. In this study, bibliometric analyses were carried out to determine the place of sentiment analysis in the context of service quality in the literature. To achieve the aim of the study, first, a bibliometric analysis of studies related to sentiment analysis was carried out. Then, a bibliometric analysis of the use of sentiment analysis in the context of service quality was carried out, and the sentiment analysis technique was evaluated in the context of service quality in the literature. In addition to bibliometric analyses, the titles and abstracts of sentiment analysis studies in the context of service quality were analyzed using content analysis to determine the subjects and techniques of the studies. It was noted that the use of the sentiment analysis technique in the field of service quality began in 2016 and that there are still few studies in this area, and that it is used in certain sectors.

Anahtar Kelimeler

Sentiment Analysis, Service Quality, Bibliometric, Content Analysis

Kaynakça

• [1] Groos, O.V., Pritchard A. (1969). Documentation notes, Journal of Documentation, 25(4), 344–349, doi:10.1108/eb026482.
• [2] Thompson, D.F., Walker, C.K. (2015). A Descriptive and Historical Review of Bibliometrics with Applications to Medical Sciences, Pharmacotherapy, 35(6), 551–559, doi:10.1002/phar.1586.
• [3] Yang, K., Hu, Y., Qi, H. (2022). Digital Health Literacy: Bibliometric Analysis, J Med Internet Res, 24(7), doi:10.2196/35816.
• [4] Hallinger, P., Kovačević, A. (2019). A Bibliometric Review of Research on Educational Administration: Science Mapping the Literature 1960 to 2018, Review of Educational Research, 89(3), 335–369, doi: 10.3102/0034654319830380.
• [5] Ercan, F., Geçmişi, M. (2020). Bibliometric Analysis of Articles on Gastronomic Tourism in Turkey, Journal of Tourism and Gastronomy Studies, 2, 1058–1075, doi:10.21325/jotags.2020.595.
• [6] Hotamişli, M., Erem, I. (2014). Muhasebe ve Finansman Dergisi’nde Yayınlanan Makalelerin Bibliyometrik Analizi, Muhasebe ve Finansman Dergisi, 63, 1-20, doi:10.25095/mufad.396474.
• [7] Kokol, P., Vosner, H.B., Zavrsnik, J. (2020). Application of Bibliometrics in Medicine: A Historical Bibliometrics Analysis, Health Information and Libraries Journal, 38(3), 125-138, doi:10.1111/hir.12295.
• [8] Karasözen, B., Bayram, Ö., Zan, B.U. (2011). Comparison of the WoS and Scopus Databases, Türk Kütüphaneciliği, 25(2), 238–260.
• [9] Eren, A., Eren, D. (2020). Bibliometric Analysis of Electronic Word-Of-Mouth Communication in Marketing Literature, 12(3), 2515–2530, doi:10.20491/isarder.2020.990.
• [10] Waltman, L., Van Eck, N.J., Van Leeuwen, N.T., (2013). Visser MS, Some Modifications to the SNIP Journal Impact Indicator, Journal of Informetrics, 7(2), 272–285, doi:10.1016/J.JOI.2012.11.011.
• [11] Van Eck, N.J., Waltman, L., (2010). Software survey: VOSviewer, a Computer Program for Bibliometric Mapping, Scientometrics, 84(2), 523–538, doi:10.1007/s11192-009-0146-3.
• [12] Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, W.M., (2021). How to Conduct a Bibliometric Analysis: An Overview and Guidelines, Journal of Business Research, 133, 285–296, doi:10.1016/j.jbusres.2021.04.070.
• [13] Chiny, M., Chihab, M., Bencharef, O., Chihab, Y. (2021). Analysis of Sentiments Conveyed Through Twitter Concerning COVID-19, SHS Web of Conferences, 119, 7003, doi:10.1051/shsconf/202111907003.
• [14] Ravi, K., Ravi, V., Siddeshwar, V., Mohan, L. (2015). Sentiment analysis applied to educational sector, 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 19-12 December 2015, doi: 10.1109/ICCIC.2015.7435667.
• [15] Kayan Ürgün, G., Çilingir Ük, Z. (2022), Integrating Servqual and Kano Models with QFD in Service Quality Improvement: An Application in the Airline Industry, Güncel Turizm Araştırmaları Dergisi, 546–572, doi:10.32572/guntad.1103387.
• [16] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis, Computational Linguistics, 37(2), 267–307, doi:10.1162/COLI_a_00049.
• [17] Medhat, W., Hassan, A., Korashy, H. (2014). Sentiment Analysis Algorithms and Applications: A Survey, Ain Shams Engineering Journal, 5(4), 1093–1113, doi:10.1016/J.ASEJ.2014.04.011.
• [18] Ravi, K., Ravi, V. (2015). A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications, Knowledge-Based Systems, 89, 14–46, doi:10.1016/J.KNOSYS.2015.06.015.
• [19] Abbasi, A., Chen, H., Salem, A. (2008). Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums, 26(3), 1-34, doi: 10.1145/1361684.1361685.
• [20] Cambria, E., Schuller, B., Xia, Y., Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis, IEEE Intelligent Systems, 28(2), 15-21, doi:10.1109/MIS.2013.30.
• [21] Kiritchenko, S., Zhu, X., Mohammad, S.M. (2014). Sentiment Analysis of Short Informal Texts, Journal of Artificial Intelligence Research, 50, 723–762, doi:10.1613/jair.4272.
• [22] Chen, T., Xu, R., He, Y., Wang, X. (2017). Improving Sentiment Analysis via Sentence Type Classification Using BiLSTM-CRF and CNN, Expert Systems with Applications, 72, 221–230, doi:10.1016/J.ESWA.2016.10.065.
• [23] Prabowo, R., Thelwall, M. (2009). Sentiment Analysis: A Combined Approach, Journal of Informetrics, 3(2), 143–157, doi:10.1016/J.JOI.2009.01.003.
• [24] Schouten, K., Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis, IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830, doi:10.1109/TKDE.2015.2485209.
• [25] Li, N., Wu, D.D. (2010)Using Text Mining and Sentiment Analysis for Online Forums Hotspot Detection and Forecast, Decision Support Systems, 48, 2, 354–368, doi:10.1016/J.DSS.2009.09.003.
• [26] Liang, T.P., Li, X., Yang, C.T., Wang, M. (2016). What in Consumer Reviews Affects the Sales of Mobile Apps: A Multifacet Sentiment Analysis Approach, International Journal of Electronic Commerce, 20(2), 236–260, doi:10.1080/10864415.2016.1087823.
• [27] Martin-Domingo, L., Martín, J.C., Mandsberg, G. (2019). Social Media as a Resource for Sentiment Analysis of Airport Service Quality (ASQ), Journal of Air Transport Management, 78, 106–115, doi:10.1016/j.jairtraman.2019.01.004.
• [28] Gitto, S., Mancuso, P. (2017). Improving Airport Services Using Sentiment Analysis of the Websites, Tourism Management Perspectives, 22, 132–136, doi:10.1016/j.tmp.2017.03.008.
• [29] Yu, C.E., Zhang, X. (2020). The Embedded Feelings in Local Gastronomy: A Sentiment Analysis of Online Reviews, Journal Of Hospitality And Tourism Technology, 11(3), 461–478, doi: 10.1108/JHTT-02-2019-0028.
• [30] Jain, P.K., Quamer, W., Pamula, R., Saravanan, V. (2021). SpSAN: Sparse Self-Attentive Network-Based Aspect-Aware Model for Sentiment Analysis, Journal of Ambient Intelligence and Humanized Computing, doi:10.1007/s12652-021-03436-x.
• [31] Rasool, G., Pathania, A. (2021). Reading Between the Lines: Untwining Online User-Generated Content Using Sentiment Analysis, Journal Of Research In Interactive Marketing, 15(3), 401–418, doi:10.1108/JRIM-03-2020-0045.
• [32] Tokarchuk, O., Barr, J.C., Cozzio, C. (2022). How Much Is Too Much? Estimating Tourism Carrying Capacity in Urban Context Using Sentiment Analysis, Tourism Management, 91(104522), doi:10.1016/j.tourman.2022.104522.
• [33] Gang, Z., Chenglin, L. (2021). Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews, Journal Of Organizational And End User Computing, 33(6), doi:10.4018/JOEUC.20211101.oa8.
• [34] Wang, Z., Wang, L., Ji, Y., Zuo, L., Qu, S. (2022). A Novel Data-Driven Weighted Sentiment Analysis Based on Information Entropy for Perceived Satisfaction, Journal of Retailing and Consumer Services, 68(103038), doi:10.1016/j.jretconser.2022.103038.
• [35] Agarwal, S. (2022). Deep Learning-Based Sentiment Analysis: Establishing Customer Dimension as the Lifeblood of Business Management, Global Business Review, 23(1), 119–136, doi:10.1177/0972150919845160.