Evaluation of Customer Satisfaction about Telecom Operators in Turkey by Analyzing Sentiments of Customers through Twitter

Main Article Content

Doğukan Kündüm
Zeynep Hilal Kilimci
Mitat Uysal
Ozan Uysal


Sentiment analysis is the process of determining whether a piece of writing is positive, negative or neutral. Text analysis based sentiment analysis consolidates natural language processing models and machine learning techniques to determine sentiment scores to the entities, topics, themes and categories within a phrase or, sentence. Furthermore, customer satisfaction is an evaluation of how products and services supplied by a company satisfy or exceed customer expectation. In this work, we propose to analyze customer satisfaction of three big telecommunication operators which are Turkcell, Turk Telekom, and Vodafone in Turkey by utilizing sentiment analysis of customers of them. For this purpose, Twitter social media platform is used for the purpose of gathering the related tweets that are mentioned with hashtags by the customers of operators. In order to improve the system performance, various pre-processing models are used such as removing punctuation marks, stop-words elimination, removing tags, URLs filter, stemming. Finally, sentiment of users is evaluated through machine learning algorithms namely, random forest, support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors (k-NN), naive Bayes (NB), and decision tree. The experiment results present remarkable classification performance with accuracy of over 80 percent for all telecom operators. Thus, this study can inspire telecommunications companies to analyze customer satisfaction through the social media platform.


Download data is not yet available.

Article Details

How to Cite
D. Kündüm, Z. H. Kilimci, M. Uysal, and O. Uysal, “Evaluation of Customer Satisfaction about Telecom Operators in Turkey by Analyzing Sentiments of Customers through Twitter”, DataSCI, vol. 3, no. 2, pp. 15-20, Dec. 2020.
Research Articles


[1] The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.

[2] Humera Shaziya, G.Kavitha, Raniah Zaheer, 2015, Text Categorization of Movie Reviews for Sentiment Analysis, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, Issue11.

[3] Ahmed, S., Pasquier, M. and Qadah, G. 2013. Key issues in conducting sentiment analysis on Arabic social media text. In 9th International Conference on Innovations in Information Technolog (IIT), (Abu Dhabi, UAE, March 17-19, 2013). IEEE, 72-77. DOI= 10.1109/Innovations.2013.6544396.

[4] Mountassir, A., Benbrahim, H. and Berrada, I., 2012. In Bramer, M., and Petridis, M, A cross-study of Sentiment Classification on Arabic corpora. Research and Development in Intelligent Systems XXIX, Springer London ,2012, 259-272.

[5] Abdulla, N., Majdalawi, R., Mohammed, S., AlAyyoub, M. and Al-Kabi, M. 2014. Automatic lexicon construction for Arabic sentiment analysis. In Proceedings of the Future Internet of Things and Cloud (FiCloud), (Barcelona,Spain, 2014). IEEE , 547-552. DOI= 10.1109/FiCloud.2014.95.

[6] Ohana, B. and Tierney, B. 2009. Sentiment classification of reviews using SentiWordNet. In Proceedinf of the 9th. IT & T Conference, (Dublin, Ireland, October 22-23,2009). Dublin Institute of Technology, 13.

[7] Abdul-Mageed,M., Kübler, S. and Diab, M. 2014. SAMAR: A system for subjectivity and sentiment analysis of Arabic social media. Computer Speech and Language. 28, 1 (January. 2014),20-37.

[8] Ali Mustafa Qamar, Suliman A. Alsuhibany and Syed Sohail Ahmed, "Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies" in International Journal of Advanced Computer Science and Applications, Vol. 8, No. 1, 2017, pp. 395-401.

[9] D. Zimbra, M. Ghiassi, and S. Lee, “Brand-related Twitter sentiment analysis using feature engineering and the dynamic architecture for artificial neural networks” in 49th Hawaii International Conference on System Sciences, HICSS 2016, Koloa, HI, USA, January 5-8, 2016, 2016, pp. 1930–1938.

[10] Latifah Almuqren and Alexandra I. Cristea, "Twitter Analysis to Predict the Satisfaction of Telecom Company Customers", Journal of Big Data, 2019, Springer.

[11] Akshay Amolik, Niketan Jivane, Mahavir Bhandari, Dr .M. Venkatesan, Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques, School of Computer Science and Engineering, VIT University, Vellore.

[12] Mesut Kaya, Guven Fidan, Ismail Toroslu, Sentiment Analysis of Turkish Political News, DOI: 10.1109/WI-IAT.2012.115, Conference: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01.

[13] A.Onan, “Sarcasm identification on twitter: a machine learning approach”, in Proceedings of CSOC 2017, Germany, 2017, pp.374-383.

[14] Tsigkritis, T. , Groumas, G. and Schneider, M. (2018) On the Use of k-NN in Anomaly Detection. Journal of Information Security, 9, 70-84. doi: 10.4236/jis.2018.91006.

[15] Martín-Valdivia M T, Rushdi Saleh M, Ureña-López L A, MontejoRáez A, “Experiments with SVM to classify opinions in different domains”, Expert Systems with Applications, 38(12), 14799- 14804, 2011.

[16] J.Ren, S.D.Lee, X.Chen, B.Kao, R.Cheng, D.Cheung, “Naive Bayes Classification of Uncertain Data", Ninth IEEE International Conference on Data Mining, 2009. ICDM ’09, pp. 944 – 949

[17] C. Horn, "Analysis and Classification," Graz University of Technology, Graz, Austria, 2010.

[18] Nehal Mamgai, Ekta Meht, Ankush Mittal, Gaurav Bhatt, “Sentiment analysis of top colleges in India using Twitter data”, International Conference on Computational Techniques in Information and Communication Technologies, 2016.

[19] Breiman, L., Friedman, J.H., Olshen, R.A. and Stone C.J. (1984) Classification and Regression Trees. Wadsworth, Belmont, CA.

[20] Ho, Tin Kam (1995). Random Decision Forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282.

[21] (July 23th 2019) A. A. Akın, Zemberek NLP Available url: https://github.com/ahmetaa/zembereknlp

[22] Oscar Araque, Ignacio Corcuera-Platas, J. Fernando Sánchez-Rada and Carlos A. Iglesias, "Enhancing deep learning sentiment analysis with ensemble techniques in social applications”, Expert Systems with Applications, vol. 77, pp. 236-246, 2017.