Evaluation of Customer Satisfaction about Telecom Operators in Turkey by Analyzing Sentiments of Customers through Twitter
Öz
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.
Anahtar Kelimeler
Sentiment Analysis, Customer Satisfaction, Random Forest, Support Vector Machines, Multilayer Perceptron, Telecom Operator
Kaynakça
- [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, Issue 11.
- [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 Technology (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, 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 Proceedings 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, 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.