Banking Transaction Metaphors Using Text Classification : A Study

Authors(2) :-Pallavi D. Bankar, Prof. Anil V. Deorankar

This study analyzes the significant determinants of a bank choice by a customer in the banking industry. Given the significance of customers as the most important resources of associations, customer maintenance is by all accounts a fundamental, essential necessity for any association. Banks are no special case for this standard. The competitive environment within which electronic banking administrations are given by various banks increases the need of customer maintenance. Text classification is a significant errand in natural language processing with wide applications. Conventional text classification strategies manually separate the highlights which are subsequently taken care of into the classifier for training. Conventional text portrayal strategies have been effectively applied to independent records of medium size. Be that as it may, information in short texts is regularly insufficient, due, for instance, to the utilization of memory helpers, which makes them difficult to classify. Subsequently, the particularities of explicit domains should be abused.

Authors and Affiliations

Pallavi D. Bankar
Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India
Prof. Anil V. Deorankar
Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India

Banking, personal finance management, text classification, natural language processing.

  1. M. R. Islam and M. A. Habib, ``A data mining approach to predict prospective business sectors for lending in retail banking using decision tree,'' CoRR, vol. abs/1504.02018, no. 2, pp. 13-22, 2015.
  2. D.-T. Vo and Y. Zhang, ``Target-dependent Twitter sentiment classification with rich automatic features,'' in Proc. IJCAI, 2015, pp. 1347-1353.
  3. Y. Cai, W.-H. Chen, H.-F. Leung, Q. Li, H. Xie, R. Y. K. Lau, H. Min, and F. L. Wang, ``Context-aware ontologies generation with basic level concepts from collaborative tags,'' Neurocomputing, vol. 208, pp. 25-38, Oct. 2016.
  4. Q. Du, H. Xie, Y. Cai, H.-F. Leung, Q. Li, H. Min, and F. L. Wang, ``Folksonomy-based personalized search by hybrid user profiles in multiple levels,'' Neurocomputing, vol. 204, pp. 142-152, Sep. 2016.
  5. M. Medvedeva, M. Kroon, and B. Plank, ``When sparse traditional models outperform dense neural networks: The curious case of discriminating between similar languages,'' in Proc. VarDial, 2017, pp. 156-163.
  6. S. Malmasi, K. Evanini, A. Cahill, J. Tetreault, R. Pugh, C. Hamill, D. Napolitano, and Y. Qian, ``A report on the 2017 native language identification shared task,'' in Proc. 12th Workshop Innov. Use NLP Building Educ. Appl., 2017, pp. 62-75.
  7. A. Keramati, H. Ghaneei, and S. M. Mirmohammadi, ``Developing a prediction model for customer churn from electronic banking services using data mining,'' Financial Innov., vol. 2, no. 1, p. 10, Dec. 2016.
  8. K. Chen, Y.-H. Hu, and Y.-C. Hsieh, ``Predicting customer churn from valuable B2B customers in the logistics industry: A case study ,'' Inf. Syst. e-Bus. Manage., vol. 13, no. 3, pp. 475-494, Aug. 2015.
  9. B. Plank, ``All-in-1: Short text classification with one model for all languages,'' in Proc. Int. Joint Conf. Natural Lang. Process. (Shared Task 4), Dec. 2017.
  10. R. Vahidov and X. He, ``Situated DSS for personal finance management: Design and evaluation,'' Inf. Manage., vol. 46, no. 8, pp. 453-462, Dec. 2009.
  11. D. A. Zetzsche, D. W. Arner, R. P. Buckley, and R. H. Weber, ``The future of data-driven finance and RegTech: Lessons from EU big bang II,'' Australas. Legal Inf. Inst., Univ. New SouthWales Law Res. Ser., Sydney, NSW, Australia, Tech. Rep., 2019.
  12. R. Wang, Z. Li, J. Cao, T. Chen, and L. Wang, ``Convolutional recurrent neural networks for text classification,'' in Proc. AAAI, vol. 333, 2015, pp. 2267-2273.
  13. C. dos Santos and M. Gatti, ``Deep convolutional neural networks for sentiment analysis of short texts,'' in Proc. 25th Int. Conf. Comput. Linguistics (COLING), 2014, pp. 69-78.
  14. R. G. Rossi, A. D. A. Lopes, and S. O. Rezende, ``Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts,'' Inf. Process. Manage., vol. 52, no. 2, pp. 217-257, Mar. 2016.
  15. S. Temma, M. Sugii, and H. Matsuno, ``The document similarity index based on the Jaccard distance for mail filtering,'' in Proc. 34th Int. Tech. Conf. Circuits/Syst., Comput. Commun. (ITC-CSCC), Jun. 2019, pp. 1-4.
  16. D. Gupta, P. Lenka, H. Bedi, A. Ekbal, and P. Bhattacharyya, ``IITP at IJCNLP-2017 task 4: Auto analysis of customer feedback using CNN and GRU network,'' in Proc. IJCNLP, 2017, pp. 184-193.
  17. SILVIA GARCÍA-MÉNDEZ, MILAGROS FERNÁNDEZ-GAVILANES, JONATHAN JUNCAL-MARTÍNEZ, FRANCISCO JAVIER GONZÁLEZ-CASTAÑO, AND ÓSCAR BARBA SEARA, “Identifying Banking Transaction Descriptions via Support Vector Machine Short-Text Classification Based on a Specialized Labelled Corpus”, IEEE, VOLUME 8, April 13, 2020.

Publication Details

Published in : Volume 8 | Issue 3 | May-June 2021
Date of Publication : 2021-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 544-548
Manuscript Number : IJSRSET2183301
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Pallavi D. Bankar, Prof. Anil V. Deorankar, " Banking Transaction Metaphors Using Text Classification : A Study, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 3, pp.544-548, May-June-2021. Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET2183301

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