The Customer is Always Right: Sentiment Analysis for Bank Service Quality

About the authors:

Elena G. Brunova, Dr. Sci. (Philol.), Professor, Department of the English Language, Military Historian, University of Tyumen; egbrunova@mail.ru; ORCID: 0000-0002-8493-5932

Yulia V. Bidulya, Cand. Sci. (Philol.), Associate Professor, Department of Information Systems, University of Tyumen; bidulya@yandex.ru; ORCID: 0000-0003-1878-3114

Abstract:

The purpose of this research is to develop a rule-based algorithm for sentiment analysis. The dataset comprises the reviews in Russian on the bank service quality from clients’ bank rating, www.banki.ru. Sentiment analysis is considered as the classification task, i.e. matching a text with one of the two classes: with positive or negative polarity. The algorithm is based on the use of certain lexical and syntactic structures, along with the sentiment lexicon consisting of positive and negative lexicons, as well as three service classes. The efficiency of the proposed algorithm is estimated with Precision, Recall and F-measure in comparison with the results of another algorithm widely used for sentiment analysis — the Naive Bayes Classifier. To estimate the efficiency, the dataset of 200 reviews on the bank service quality is used. The values of Precision, Recall and F-measure for the proposed algorithm are 5-8% higher than for the Naïve Bayes Classifier.

References:

1. Annett M., Kondrak G. 2008. “A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs”. In: Advances in Artificial Intelligence, pp. 25-35. Springer Berlin Heidelberg.
2. Brunova E. 2013. “Parametrical Words in the Sentiment Lexicon”. International Journal of Cognitive Research in Science, Engineering and Education, vol. 1, no 2. http://www.ijcrsee.com
3. Brunova E. G., Bidulya Yu. V. 2014. “Algoritm s elementami for-mal'noy grammatiki dlya kontent-analiza mneniy” [Algorithm with Formal Grammar Elements for Sentiment Analysis]. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, no 7, pp. 242-250. https://vestnik.utmn.ru/energy/vypuski-arhiv/2014/113083/
4. Ganapathibhotla M., Liu B. 2008. “Mining Opinions in Comparative Sentences”. Proceedings of the 22nd International Conference on Computational Linguistics, pp. 241-248. Manchester.
5. Hatzivassiloglou V., McKeown K. 1997. “Predicting the Semantic Orientation of Adjectives”. Proceedings of the 35th Annual Meeting of ACL, pp. 174-181. Madrid.
6. Liu B. 2010. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing. http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf
7. Lukashevich N. V., Chetverkin I. I. 2011. “Izvlechenie i ispol'zovanie otsenochnykh slov v zadache klassifikatsii otzyvov na tri klassa” [Retrieval and Application of Sentiment Lexicon in the Context of Reviews Classifying into Three Classes]. Vychislitel'nye metody i programmirovanie, vol. 12, pp. 73-81.
8. Manning С., Raghavan P., Schütze H. 2008. Introduction to Information Retrieval. Cambridge: Cambridge University Press.
9. Medhat W., Hassan A., Korashy H. 2014. “Sentiment Analysis Algorithms and Applications: A Survey”. Ain Shams Engineering Journal, vol. 5, no 4, pp. 1093-1113.
10. Nasukawa T., Yi J. 2003. “Sentiment Analysis: Capturing Favorability Using Natural Language Processing”. Proceedings of the 2nd International Conference on Knowledge Capture, pp. 70-77. Florida.
11. Pal J., Saha A. 2011. “Identifying Themes in Social Media and Detecting Sentiments”. International Journal of Statistics and Applications, vol. 1, no. 1, pp. 14-19.
12. Pang B., Lee L., Vaithyanathan S. 2002. “Thumbs Up? Sentiment Classification Using Machine Learning Techniques”. Proceedings of EMNLP. http://www.cs.cornell.edu/people/pabo/papers/sentiment.pdf
13. RGGU. 2015. “Kompyuternaya lingvistika i intellektual'nye tehnologii” [Computer Linguistics and Intellectual Technologies]. Proceedings of the Annual International Conference “Dialogue”, vol. 2, no 14(21). Moscow: RGGU.
14. Serov V. (comp.). 2003.Entsiklopedicheskiy slovar' krylatykh slov i vyrazheniy [Encyclopedia Dictionary of Popular Expressions]. Moscow: Lokid-Press.
15. Tan Li Im, Phang Wai San, Chin Kim On, Patricia Anthony. 2015. “Rule-Based Sentiment Analysis for Financial News”. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 1601-1606. Hong Kong.
16. Turney P. 2002. “Thumbs Up or thumbs Down? Semantic Orientation Applied to Supervised Classification of Reviews”. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417-424. Philadelphia.
17. Webb G., Boughton J., Wang Z. 2005. Not So Naive Bayes: Aggregating One-Dependence Estimators // Machine Learning, 58, P. 5-24.