Skip to main content
SpringerLink
Log in

Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-objective Particle Swarm Optimization

  • Chapter
  • First Online:
Nature Inspired Computing for Data Science

Part of the book series: Studies in Computational Intelligence ((SCI,volume 871))

Abstract

The ease of access to social media and its wide reach has made it the preferred platform for consumers to express their opinions and grievances regarding all types of products and services. As such, it serves as a fertile ground for organizations to deploy analytical customer relationship management (ACRM) and generate business insights of tremendous value. Of particular importance is identifying and quickly resolving customer complaints. If not redressed, these complaints could lead to customer churn and when addressed quickly and efficiently, they can double the profits. This automatic product-wise clustering of complaints helps in better sentiment analysis on products and services. In this paper, two variants of a novel multi-objective clustering algorithm are proposed with applications to sentiment analysis, an important area of analytical CRM in banking industry. The first variant, MOPSO-CD-Kmeans, employs Multi-objective Particle Swarm Optimization along with heuristics of K-means and the second variant, MOPSO-CD-SKmeans employs the same multi-objective particle swarm optimization along with the heuristics of Spherical K-means to find an optimal partitioning of the data. Two clustering criteria were considered as objective functions to be optimized to find a set of Pareto optimal solutions, and then the Silhouette Index was employed to determine the optimal number of clusters. The algorithm is then tested on bank based complaint datasets related to four Indian banks. Experiments indicate that MOPSO-CD-SKmeans is able to achieve promising results in terms of product-wise clustering of complaints and could outperform the first variant.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ravi, K., and V. Ravi. 2015. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Syst 89: 14–46. https://doi.org/10.1016/J.KNOSYS.2015.06.015.

    Article  Google Scholar 

  2. Macqueen, J. 1967. Some methods for classification and analysis of multivariate observations. In 5-th Berkeley symposium on mathematical statistics and probability, 281–297.

    Google Scholar 

  3. Dhillon, I.S., and D.S. Modha. 2001. Concept decompositions for large sparse text data using clustering. Machine Learning 42: 143–175. https://doi.org/10.1023/A:1007612920971.

    Article  MATH  Google Scholar 

  4. Li, G., and F. Liu. 2010. A clustering-based approach on sentiment analysis. In 2010 IEEE international conference on intelligent systems and knowledge engineering, 331–337. IEEE.

    Google Scholar 

  5. Hadano, M., K. Shimada, and T. Endo. 2011. Aspect identification of sentiment sentences using a clustering algorithm. Procedia-Social and Behavioral Sciences 27: 22–31. https://doi.org/10.1016/J.SBSPRO.2011.10.579.

    Article  Google Scholar 

  6. Zhai, Z., B. Liu, H. Xu, and P. Jia. 2011. Clustering product features for opinion mining. In Proceedings of the fourth ACM international conference on web search and data mining-WSDM ’11, 347. New York, USA: ACM Press.

    Google Scholar 

  7. Farhadloo, M., and E. Rolland. 2013. Multi-class sentiment analysis with clustering and score representation. In 2013 IEEE 13th international conference on data mining workshops, 904–912. IEEE.

    Google Scholar 

  8. Souza, E., A.L.I. Oliveira, G. Oliveira, et al. 2016. An unsupervised particle swarm optimization approach for opinion clustering. In 2016 5th Brazilian conference on intelligent systems (BRACIS), 307–312. IEEE.

    Google Scholar 

  9. Chandra Pandey, A., D. Singh Rajpoot, and M. Saraswat. 2017. Twitter sentiment analysis using hybrid cuckoo search method. Information Processing and Management 53: 764–779. https://doi.org/10.1016/J.IPM.2017.02.004.

    Article  Google Scholar 

  10. Souza, E., D. Santos, G. Oliveira, et al. 2018. Swarm optimization clustering methods for opinion mining. Natural Computing, 1–29. https://doi.org/10.1007/s11047-018-9681-2.

  11. Fong, S., E. Gao, and R. Wong. 2015. Optimized swarm search-based feature selection for text mining in sentiment analysis. In 2015 IEEE international conference on data mining workshop (ICDMW), 1153–1162. IEEE.

    Google Scholar 

  12. Alshari, E.M., A. Azman, S. Doraisamy, et al. 2017. Improvement of sentiment analysis based on clustering of Word2Vec features. In 2017 28th international workshop on database and expert systems applications (DEXA), 123–126. IEEE.

    Google Scholar 

  13. Mayank, D., K. Padmanabhan, and K. Pal. 2016. Multi-sentiment modeling with scalable systematic labeled data generation via Word2Vec clustering. In 2016 IEEE 16th international conference on data mining workshops (ICDMW), 952–959. IEEE.

    Google Scholar 

  14. Xiong, S., and D. Ji. 2016. Exploiting flexible-constrained K-means clustering with word embedding for aspect-phrase grouping. Information Sciences (Ny) 367–368: 689–699. https://doi.org/10.1016/J.INS.2016.07.002.

    Article  Google Scholar 

  15. Ma, B., H. Yuan, and Y. Wu. 2017. Exploring performance of clustering methods on document sentiment analysis. Journal of Information Science 43: 54–74. https://doi.org/10.1177/0165551515617374.

    Article  Google Scholar 

  16. Mukhopadhyay, A., U. Maulik, and S. Bandyopadhyay. 2015. A survey of multiobjective evolutionary clustering. ACM Computing Surveys 47: 1–46. https://doi.org/10.1145/2742642.

    Article  Google Scholar 

  17. Handl, J., and J. Knowles. 2004. Evolutionary multiobjective clustering, vol. 3242. Lecture Notes Computer Science, 1081–1091. https://doi.org/10.1007/978-3-540-30217-9_109.

    Google Scholar 

  18. Handl, J., and J. Knowles. 2005. Multiobjective clustering around medoids. In 2005 IEEE congress on evolutionary computation, 632–639. IEEE.

    Google Scholar 

  19. Handl, J., and J. Knowles. 2007. An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 11: 56–76. https://doi.org/10.1109/TEVC.2006.877146.

    Article  Google Scholar 

  20. Bandyopadhyay, S., U. Maulik, and A. Mukhopadhyay. 2007. Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 45: 1506–1511. https://doi.org/10.1109/TGRS.2007.892604.

    Article  Google Scholar 

  21. Won, J.-M., S. Ullah, and F. Karray. 2008. Data clustering using multi-objective hybrid evolutionary algorithm. In 2008 international conference on control, automation and systems, 2298–2303. IEEE.

    Google Scholar 

  22. Mukhopadhyay, A., U. Maulik, and S. Bandyopadhyay. 2009. Multiobjective genetic clustering with ensemble among pareto front solutions: application to MRI brain image segmentation. In 2009 seventh international conference on advances in pattern recognition, 236–239. IEEE.

    Google Scholar 

  23. Özyer, T., M. Zhang, and R. Alhajj. 2011. Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Applied Intelligence 35: 110–122. https://doi.org/10.1007/s10489-009-0206-7.

    Article  Google Scholar 

  24. Folino, F., and C. Pizzuti. 2010. A multiobjective and evolutionary clustering method for dynamic networks. In 2010 international conference on advances in social networks analysis and mining, 256–263. IEEE.

    Google Scholar 

  25. Mukhopadhyay, A., U. Maulik, and S. Bandyopadhyay. 2013. An interactive approach to multiobjective clustering of gene expression patterns. IEEE Transactions on Biomedical Engineering 60: 35–41. https://doi.org/10.1109/TBME.2012.2220765.

    Article  Google Scholar 

  26. Shirakawa, S., and T. Nagao. 2009. Evolutionary image segmentation based on multiobjective clustering. In 2009 IEEE congress on evolutionary computation, 2466–2473. IEEE.

    Google Scholar 

  27. Bandyopadhyay, S., U. Maulik, and R. Baragona. 2010. Clustering multivariate time series by genetic multiobjective optimization. Metron 68: 161–183. https://doi.org/10.1007/BF03263533.

    Article  MathSciNet  MATH  Google Scholar 

  28. Coelho, A.L.V., E. Fernandes, and K. Faceli. 2010. Inducing multi-objective clustering ensembles with genetic programming. Neurocomputing 74: 494–498. https://doi.org/10.1016/J.NEUCOM.2010.09.014.

    Article  Google Scholar 

  29. Saha, S., and S. Bandyopadhyay. 2008. A new multiobjective simulated annealing based clustering technique using stability and symmetry. In 2008 19th international conference on pattern recognition, 1–4. IEEE.

    Google Scholar 

  30. Xue, F., A.C. Sanderson, and R.J. Graves. 2005. Multi-objective differential evolution-algorithm, convergence analysis, and applications. In 2005 IEEE congress on evolutionary computation, 743–750. IEEE.

    Google Scholar 

  31. Kennedy, J., and R. Eberhart (1995) Particle swarm optimization. In IEEE international conference on neural networks, vol. 4, 1942–1948.

    Google Scholar 

  32. Agrawal, S., B.K. Panigrahi, and M.K. Tiwari. 2008. multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch. IEEE Transactions on Evolutionary Computation 12: 529–541. https://doi.org/10.1109/TEVC.2007.913121.

    Article  Google Scholar 

  33. Paoli, A., F. Melgani, and E. Pasolli. 2009. Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing 47: 4175–4188. https://doi.org/10.1109/TGRS.2009.2023666.

    Article  Google Scholar 

  34. Nanda, S.J., and G. Panda. 2013. Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models. Engineering Applications of Artificial Intelligence 26: 1429–1441. https://doi.org/10.1016/j.engappai.2012.11.008.

    Article  Google Scholar 

  35. Deb, K., and D. Kalyanmoy. 2001. Multi-objective optimization using evolutionary algorithms. Wiley.

    Google Scholar 

  36. Coello Coello, C.A., and M.S. Lechuga. 2002. MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 congress on evolutionary computation, CEC ’02, 1051–1056. IEEE. (Cat. No.02TH8600).

    Google Scholar 

  37. Raquel, C.R., and P.C. Naval. 2005. An effective use of crowding distance in multiobjective particle swarm optimization. In Proceedings of the 2005 conference on genetic and evolutionary computation-GECCO ’05, 257. New York, USA: ACM Press.

    Google Scholar 

  38. Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6: 182–197. https://doi.org/10.1109/4235.996017.

    Article  Google Scholar 

  39. Caliński, T., and J. Harabasz. 1974. A dendrite method for cluster analysis. Communications in Statistics-Theory and Methods 3, 1–27. https://doi.org/10.1080/03610927408827101.

    Article  MathSciNet  Google Scholar 

  40. Rousseeuw, P.J. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20: 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.

    Article  MATH  Google Scholar 

  41. Ravi, K., V. Ravi, and P.S.R.K. Prasad. 2017. Fuzzy formal concept analysis based opinion mining for CRM in financial services. Applied Soft Computing 60: 786–807. https://doi.org/10.1016/J.ASOC.2017.05.028.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Excellence in Analytics, Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1, Masab Tank, Hyderabad, 500057, India

    Rohit Gavval & Vadlamani Ravi

  2. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Rohit Gavval

Authors
  1. Rohit Gavval
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vadlamani Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vadlamani Ravi .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Minakhi Rout

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Jitendra Kumar Rout

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Himansu Das

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gavval, R., Ravi, V. (2020). Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-objective Particle Swarm Optimization. In: Rout, M., Rout, J., Das, H. (eds) Nature Inspired Computing for Data Science. Studies in Computational Intelligence, vol 871. Springer, Cham. https://doi.org/10.1007/978-3-030-33820-6_9

Download citation

Publish with us

Policies and ethics

Search

Navigation