Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

Year 2023, Volume: 20 Issue: 54, 473 - 487, 31.07.2023

Erhan Sur , Hüseyin Çakır

https://doi.org/10.26466/opusjsr.1272020

Abstract

Bu çalışmada, duygu analizi ve metin madenciliği teknikleri kullanılarak hizmet kalitesi ölçümü için yeni bir model önerilmiştir. Bu model, geleneksel yöntemlerin karşılaştığı zaman, maliyet ve uygulama güçlüğünün üstesinden gelmeyi ve hizmet kalitesi ölçümüne daha dinamik ve verimli bir yaklaşım sağlamayı amaçlamıştır. Ayrıca bu modelde SERVQUAL veya SERVPERF gibi hizmet kalitesi ölçümünde kullanılan boyutların yerine metin madenciliği ile modelin kullanıldığı hizmet sektörüne özel yeni kategoriler ve anahtar kelimelerin nasıl belirleneceği gösterilmiştir. Böylelikle hizmet kalitesi ölçümünde daha doğru sonuçlara ulaşılması hedeflenmemiştir. Çalışmada önerilen modelin amacına ulaşabilmesi için metin madenciliği ve duygu analiziyle işlenen sosyal medya verilerinden hizmet kalitesi ölçüm modelinin nasıl geliştirileceği sorusuna yanıt aranmıştır. Bu soruya yanıt bulabilmek için bir belediyenin Twitter hesabına 2016-2022 yılları arasında gönderilen 109.844 tweet’den metin madenciliği yöntemi ile belediyenin vermiş olduğu su ve kanalizasyon hizmetleriyle ilgili olarak “sel”, “sayaç”, “yağmur”, “sulama”, “altyapı”, “kanalizasyon”, “lağım”, “rögar”, “aski”, “susuz”, “suya” anahtar kelimeleri çıkartılmıştır. Çıkartılan anahtar kelimelerin geçtiği 5766 tweet duygu analizine tabi tutularak hizmet kalitesi ölçümü gerçekleştirilmiştir. Yapılan hizmet kalitesi ölçüm neticesinde 1922 olumsuz, 973 olumlu ve 2871 nötr tweet tespit edilmiştir. Ortalama olumsuz puan 0,51, ortalama olumlu puan 0,11 ve ortalama nötr puan 0,38 olarak hesaplanmıştır.

Keywords

duygu analizi, metin madenciliği, hizmet kalitesi, twitter veri analizi

A Proposed Service Quality Measurement Model using Sentiment Analysis and Text Mining: The Case of Water and Sewerage Services

Abstract

This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods' time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the model’s purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords "flood", "meter", "rain", "irrigation", "infrastructure", "sewerage", "sewage", "maintenance hole ", "aski", "waterless", "water" were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

Keywords

sentiment analysis, text mining, service quality, twitter data analysis

References

• Akıncı, S., Atılgan İnan, E., Aksoy, Ş., & Büyükküpcü, A. (2009). Pazarlama Literatüründe Hizmet Kalitesi Kavramının Dünü ve Bugünü. H.Ü. İktisadi ve İdari Bilimler Fakültesi Dergisi, 27(2), 61–82.
• Akyüz, F., & Gülten, S. (2022). Metin Madenciliği Kelime Muhasebesi ve Denetimi. Gazi Kitabevi.
• Ali, K., Hamilton, M., Thevathayan, C., & Zhang, X. (2022). Big social data as a service (BSDaaS): a service composition framework for social media analysis. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00620-4
• Alnawas, A., & Arıcı, N. (2018). The Corpus Based Approach to Sentiment Analysis in Modern Standard Arabic and Arabic Dialects: A Literature Review. Journal of Polytechnic. https://doi.org/10.2339/politeknik.403975
• Altunkaynak, B. (2022). Veri Madenciliği Yöntemleri ve R Uygulamaları. Şeçkin Yayıncılık.
• Andrea, A. D. ’, Ferri, F., & Grifoni, P. (2015). Approaches, Tools and Applications for Sentiment Analysis Implementation. In International Journal of Computer Applications (Vol. 125, Issue 3). http://messenger.yahoo.com/features/emoticons
• Artsin, M. (2020). Bir Metin Madenciliği Uygulaması: Vosviewer. Eskişehir Technical University Journal of Science and Technology B-Theoretical Sciences, 8(2), 344–354. https://doi.org/10.20290/estubtdb.644637
• Ay, F., & Büyükkeklik, A. (2016). Kamu Hizmetlerinde Kalite: Mardin Adalet Sarayında Bir Araştırma. Yönetim ve Ekonomi Araştırmaları Dergisi, 71–88. https://doi.org/10.11611/yead.280648
• Aydogan, E., & Ali Akcayol, M. (2016, September 19). A comprehensive survey for sentiment analysis tasks using machine learning techniques. Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016. https://doi.org/10.1109/INISTA.2016.7571856
• Beşki̇rli̇, A., Gülbandilar, E., & Dağ, İ. (2021). Metin Madenciliği Yöntemleri ile Twitter Verilerinden Bilgi Keşfi. Journal of Estudam Information, 2(1), 21–25.
• Brady, M. K., & Cronin, J. J. (2001). Some New Thoughts on Conceptualizing Perceived Service Quality: A Hierarchical Approach. Journal of Marketing, 65(3), 34–49. https://doi.org/10.1509/jmkg.65.3.34.18334
• Cemaloğlu, N., & Duykuluoğlu, A. (2020). Sosyal Bilimlerde Veri Madenciliği. Pegem Akademi.
• Chang, Y. C., Ku, C. H., & Chen, C. H. (2019). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 48, 263–279. https://doi.org/10.1016/j.ijinfomgt.2017.11.001
• Cronin, J. J., & Taylor, S. A. (1992). Measuring Service Quality: A Reexamination and Extension. Journal of Marketing, 56(3), 55–68. https://doi.org/10.1177/002224299205600304
• Duan, W., Yu, Y., Cao, Q., & Levy, S. (2016). Exploring the Impact of Social Media on Hotel Service Performance: A Sentimental Analysis Approach. Cornell Hospitality Quarterly, 57(3), 282–296. https://doi.org/10.1177/1938965515620483
• Ermokova, T., Henke, M. & fabian, B. (2021). Commerical Sentiment Analysis Solutions: A Comparative Study. In Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST 2021). 103-114. https://doi.org/10.5220/0010709400003058
• Eryılmaz, B. (2013). Kamu Yönetimi (S. Sözen, Ed.). Anadolu Üniversitesi.
• Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1). https://doi.org/10.1186/s40537-015-0015-2
• Filiz, Z., Yılmaz, V., & Yağızer, C. (2010). Belediyelerde Hizmet Kalitesinin Servqual Analizi ile Ölçümü: Eskişehir Belediyelerinde Bir Uygulama. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 10(3), 59–76.
• Flores, C. C., & Rezende, D. A. (2018). Twitter information for contributing to the strategic digital city: Towards citizens as co-managers. Telematics and Informatics, 35(5), 1082–1096. https://doi.org/10.1016/j.tele.2018.01.005
• Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40(16), 6266–6282. https://doi.org/10.1016/j.eswa.2013.05.057
• Gitto, S., & Mancuso, P. (2017). Improving airport services using sentiment analysis of the websites. Tourism Management Perspectives, 22, 132–136. https://doi.org/10.1016/j.tmp.2017.03.008
• Grönroos, C. (1984). A Service Quality Model and its Marketing Implications. European Journal of Marketing, 18(4), 36–44.
• Guetterman C. T, & James G. T. (2023). A Software Feature for Mixed Methods Analysis: The MAXQDA Interactive Quote Matrix. Methods in Psychology, 100116.
• Gümüşoğlu, Ş., Erdem, S., Kavrukkoca, G., & Özdağoğlu, A. (2003). Belediyelerde Beklenen Algılanan Hizmet Kalitesinin SERVQUAL Modeli ile Ölçülmesi ve Muğla İlinde Bir Uygulama. 3. Ulusal Üretim Araştırmaları Sempozyumu.
• Hasan, A., Moin, S., Karim, A., & Shamshirband, S. (2018). Machine Learning-Based Sentiment Analysis for Twitter Accounts. Mathematical and Computational Applications, 23(1), 11. https://doi.org/10.3390/mca23010011
• He, W., Tian, X., Hung, A., Akula, V., & Zhang, W. (2018). Measuring and comparing service quality metrics through social media analytics: a case study. Information Systems and E-Business Management, 16(3), 579–600. https://doi.org/10.1007/s10257-017-0360-0
• Hood, C., & Dixon, R. (2013). A Model of Cost-Cutting in Government? The Great Management Revolution in UK Central Government Reconsidered. Public Administration, 91(1), 114–134. https://doi.org/10.1111/j.1467-9299.2012.02072.x
• Hung, Y. H., Huang, M. L., & Chen, K. S. (2003). Service quality evaluation by service quality performance matrix. Total Quality Management and Business Excellence, 14(1), 79–89. https://doi.org/10.1080/14783360309706
• Islami, M. T. F. Al, Barakbah, A. R., & Harsono, T. (2021). Social Media Engineering for Issues Feature Extraction using Categorization Knowledge Modelling and Rule-based Sentiment Analysis. Information Journal on Informatics Visualization, 5(1), 83–93.
• Jun, M., Peterson, R. T., & Zsidisin, G. A. (1998). The Identification and Measurement of Quality Dimensions in Health Care: Focus Group Interview Results. Health Care Management Review, 23(4), 81–96.
• 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, 6(2), 546–572. https://doi.org/10.32572/guntad.1103387
• Kemp, S. (2022). Digital 2022: Global Overview Report.
• King, D., Ramirez-Cano, D., Greaves, F., Vlaev, I., Beales, S., & Darzi, A. (2013). Twitter and the health reforms in the English National Health Service. Health Policy, 110(2–3), 291–297. https://doi.org/10.1016/j.healthpol.2013.02.005
• Kotler, P., Kartajaya, H., & Setiawan, I. (2017). Pazarlama 4.0. Optimist Yayın Grubu.
• Lee, H. J., Lee, M., Lee, H., & Cruz, R. A. (2021). Mining service quality feedback from social media: A computational analytics method. Government Information Quarterly, 38(2). https://doi.org/10.1016/j.giq.2021.101571
• Levy, P., & Birkner, C. (2011). Digital Marketing 2011: What You Need to Know. Marketing News, 10–15.
• Liu, Y., Jiang, C., & Zhao, H. (2019). Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media. Decision Support Systems, 123. https://doi.org/10.1016/j.dss.2019.113079
• Losiewicz, P., Oard, D. W., & Kostoff, R. N. (2000). Textual Data Mining to Support Science and Technology Management *. In Journal of Intelligent Information Systems (Vol. 15).
• Mainka, A., Hartmann, S., Stock, W. G., & Peters, I. (2014). Government and social media: A case study of 31 informational world cities. Proceedings of the Annual Hawaii International Conference on System Sciences, 1715–1724. https://doi.org/10.1109/HICSS.2014.219
• 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. https://doi.org/10.1016/j.jairtraman.2019.01.004
• Mbassi, J. C., Mbarga, A. D., & Ndeme, R. N. (2019). Public Service Quality and Citizen-Client’s Satisfaction in Local Municipalities. Journal of Marketing Development and Competitiveness, 13(3), 110–123.
• Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
• Meral, A., & Baş, M. (2013). Türkiye’de Faaliyet Gösteren GSM Operatörlerinin Hizmet Kalitesi Bakımından Karşılaştırılması ve Uygulana Rekabet Stratejileri. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(2), 41–70.
• Mutlu, S., & Ermeç Sertoğlu, A. (2018). Düşük Maliyetli ve Tam Hizmet Sunan Havayolları Müşterilerinin Hizmet Kalitesi Beklentilerinin Karşılaştırılması - The Comparison of Service Quality Expectations of Low Cost Carriers and Full Service Carriers Customers. Journal of Business Research - Turk, 10(1), 528–550. https://doi.org/10.20491/isarder.2018.406
• Oğuzlar, A. (2011). Temel Metin Madenciliği (1st ed.). Dora Yayınları.
• Parasuraman, A., Zelthami, V. A., & Berry, L. L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49, 41–50.
• Ramanathan, R., & Karpuzcu, H. (2011). Comparing perceived and expected service using an AHP model: An application to measure service quality of a company engaged in pharmaceutical distribution. In OPSEARCH (Vol. 48, Issue 2, pp. 136–152). https://doi.org/10.1007/s12597-010-0022-1
• Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic Keyword Extraction From Individual Documents. In M. Berry & J. Kogan (Eds.), Text Mining Application and Teory. Wiley.
• Sailunaz, K., & Alhajj, R. (2019). Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36. https://doi.org/10.1016/j.jocs.2019.05.009
• Sezer, Ö. (2008). Kamu Hizmetinde Müşteri (vatandaş) Odaklılık: Türkiye’de Kamu Hizmeti Açısından Bir Değerlendirme. Zonguldak Karaelmas Üniversitesi Sosyal Bilimler Dergisi, 4(8), 147–171.
• Tedeschi, A., & Benedetto, F. (2015). A cloud-based big data sentiment analysis application for enterprises’ brand monitoring in social media streams. 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry, RTSI 2015 - Proceedings, 186–191. https://doi.org/10.1109/RTSI.2015.7325096
• Usta, R., & Memiş, L. (2010). Belediye Hizmetlerinde Kalite: Giresun Belediyesi Örneği. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(2), 333–355.
• Yang, D., Zhang, D., Yu, Z., Yu, Z., & Zeghlache, D. (2014). SESAME: Mining user digital footprints for fine-grained preference-aware social media search. ACM Transactions on Internet Technology, 14(4). https://doi.org/10.1145/2677209
• Yildirim, K. E., Yildirim, A., & Ozcan, S. (2019). Integrated Usage of the SERVQUAL and Quality Function Deployment Techniques in the Assessment of Public Service Quality: The Case of Ardahan Municipality. Business and Economics Research Journal, 10(4), 885–901. https://doi.org/10.20409/berj.2019.208
• Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919–926. https://doi.org/10.1016/j.dss.2012.12.028
• Zhan, Y., Han, R., Tse, M., Ali, M. H., & Hu, J. (2021). A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry. Technological Forecasting and Social Change, 163. https://doi.org/10.1016/j.techfore.2020.120504