Skip to main content

Predictive big data analytic on demonetization data using support vector machine


Predictive analytics is the branch of the advanced analytics which makes the user to predict the future events with current statistics. The patterns found in historical and transactional data can be used to identify risks and opportunities for future. Predictive analytics models capture relationships among many factors to assess risk with a particular set of conditions to assign a score. This paper provides predictive analysis on demonetization data using support vector machine approach (PAD-SVM). The proposed PAD-SVM system involved three stages including preprocessing stage, descriptive analysis stage, and prescriptive analysis. The pre-processing stage involves cleaning the obtained data, performing missing value treatment and splitting the necessary data from the tweets. The descriptive analysis stage involves finding the most influential people regarding this subject and performing analytical functionalities. Semantic analysis also is performed to find the sentiment values of the users and to find the compound polarity of each tweet. Predictive analysis is performed to view the current mindset of people and the society reacts to the issue in the current time. This analysis is performed to find out the overall view point of the society and their view may change in the near-future in regarding to the scheme of demonetization as well.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. Márquez-Vera, C., Morales, C.R., Soto, S.V.: Predicting school failure and dropout by using data mining techniques. IEEE J. Latin-Am. Learn. Technol. 8(1), 7–14 (2013)

    Article  Google Scholar 

  2. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT and EMNLP, vol. 5, pp. 347–354. ACL, New York (2005)

  3. Tushar, R., Srivastava, S.: Analyzing stock market movements using twitter sentiment analysis. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), vol. 6. IEEE Computer Society (2012)

  4. Jeffrey, D., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 4, 107–113 (2008)

    Google Scholar 

  5. Jimmy, L., Kolcz, A.: Large-scale machine learning at twitter. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, vol. 8, pp. 793–804. ACM, New York (2012)

  6. Jiang, B., Topaloglu, U., Yu, F.: Towards large-scale twitter mining for drug-related adverse events. In: Proceedings of the 2012 International Workshop on Smart Health and Wellbeing, vol. 2, pp. 25–32. ACM, New York (2012)

  7. Bingwei, L., Blasch, E., Chen, Y., Shen, D., Chen, G.: Scalable sentiment classification for big data analysis using Naive Bayes classifier. In: 2013 IEEE International Conference on Big Data, vol. 5, pp. 99–104. IEEE (2013)

  8. Cuesta, A., Barrero, D.F.: MD R-Moreno: A framework for massive twitter data extraction and analysis. Malays. J. Comput. Sci. 3, 50–67 (2014)

    Google Scholar 

  9. Michal, S., Romanowski, A.: Sentiment analysis of twitter data within big data distributed environment for stock prediction. In: 2015 Federated Conference on Computer Science and Information Systems, vol. 2, pp. 1349–1354. IEEE (2015)

  10. Mohit, T., Gohokar, I., Sable, J., Paratwar, D., Wajgi, R.: Multi-class tweet categorization using map reduce paradigm. Int. J. Comput. Trends Technol. 3, 78–81 (2014)

    Google Scholar 

  11. Tao, C.C., Kim, S.K., Lin, Y.A., Yu, Y.Y., Bradski, G., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. NIPS 6, 281–288 (2006)

    Google Scholar 

  12. Yingyi, B.: HaLoop: efficient iterative data processing on large clusters. In: Proceedings of the VLDB Endowment 3.1-2, vol. 6, pp. 285–296 (2010)

  13. Maite, T.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 7, 267–307 (2011)

    Google Scholar 

  14. Katkar, V.D., Kulkarni, S.V.: A novel parallel implementation of Naive Bayesian classifier for big data. In: International Conference on Green Computing, Communication and Conservation of Energy, vol. 7, pp. 847–852, ISBN 978-1-4673-6126-2/2013. IEEE (2015)

  15. Jose, A.K., Bhatia, N., Krishna, S.: Twitter Sentiment Analysis, vol. 5. National Institute of Technology Calicut, IEEE, Calicut (2010)

    Google Scholar 

  16. Wook, M., Hani Yahaya, Y., Wahab, N.: Predicting NDUM student’s academic performance using data mining techniques. In: Second International Conference on Computer and Electrical Engineering, vol. 3, pp. 357–361. IEEE (2009)

  17. Kaliappan, M., Augustine, S., Paramasivan, B.: Enhancing energy efficiency and load balancing in mobile adhoc network using dynamic genetic algorithms. J. Netw. Comput. Appl. 73, 35–43 (2016)

    Article  Google Scholar 

  18. Kaliappan, M., Mariappan, E., Prakash, M.V., Paramasivan, B.: Load balanced clustering technique in MANET using genetic algorithms. Defence Sci. J. 66(3), 251–258 (2016).

    Article  Google Scholar 

  19. Subbulakshmi, P., Vimal, S.: Secure data packet transmission in MANET using enhanced identity-based cryptography. Int. J. New Technol. Sci. Eng. 3(12), 35–42 (2016)

    Google Scholar 

  20. Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013)

    Article  Google Scholar 

  21. Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Kiem, D.A., Haug, L.E., Hsu, M.C.: Visual sentiment analysis on twitter data streams. In: IEEE Symposium on Visual Analytics Science and Technology, vol. 20, October 23, Providence, RI, USA (2014)

  22. Kaliappan, M., Paramasivam, B.: Enhancing secure routing in mobile ad hoc networks using a dynamic Bayesian signalling game model. Comput. Electr. Eng. 41, 301–313 (2015)

    Article  Google Scholar 

  23. Mariappan, E., Kaliappan, M., Vimal, S.: Energy efficient routing protocol using Grover’s searching algorithm for MANET. Asian J. Inf. Technol. 15, 4986–4994 (2016).

    Article  Google Scholar 

  24. Vimal, S., Kalaivani, L., Kaliappan, M.: Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks. Clust. Comput. (2017).

    Article  Google Scholar 

  25. Sudhakar Ilango, S., Vimal, S., Kaliappan, M., Subbulakshmi, P.: Optimization using Artificial Bee Colony based clustering approach for big data. Clust. Comput. (2018).

    Article  Google Scholar 

  26. Suresh, A., Varatharajan, R.: Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Comput. (2017).

    Article  Google Scholar 

  27. Chinnasamy, A., Sivakumar, B., Selvakumari, P., Suresh, A.: Minimum connected dominating set based RSU allocation for smartCloud vehicles in VANET. Cluster Comput. (2018).

    Article  Google Scholar 

  28. Suresh, A., Reyana, A., Varatharajan, R.: CEMulti-core architecture for optimization of energy over heterogeneous environment with high performance smart sensor devices. Wirel. Pers. Commun. (2018).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to S. Sivasubramanian.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kannan, N., Sivasubramanian, S., Kaliappan, M. et al. Predictive big data analytic on demonetization data using support vector machine. Cluster Comput 22 (Suppl 6), 14709–14720 (2019).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: