Advertisement

Opinion Mining Based on People’s Feedback About Engineering Degree

  • M. Karpagam
  • S. Kanthimathi
  • S. Akila
  • S. Kanimozhi SugunaEmail author
Conference paper
  • 213 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 44)

Abstract

Support Vector Machine (SVM) is a learning model which can be used as data analyzer for classification by its associated algorithms. SVM classifies the data by finding the hyper-plane that maximizes the gap between two classes. The structure of decision tree consists of root, branches and leaf nodes and the tests performed on an attribute and leaf nodes were represented by internal nodes which denote the result of the test. In this paper, a model to classify data using an ensemble of decision tree and support vector machine is proposed on a dataset collected on the topic of ‘Engineering Degree’. Combining the decision tree and support vector machine can be an effective method for classifying the data as it reduces the testing and training time of the data collected. The analysis of the result has been performed on a sample set of data taken from a large collection stored in the cloud using Hadoop.

Keywords

Support vector machine (SVM) Decision tree algorithm Classification Hadoop hive Engineering degree 

Notes

Compliance with Ethical Standards

All author states that there is no conflict of interest. We used our own data. Humans/animals are not involved in this research work.

References

  1. 1.
    Arun Kumar, M., Gopal, M.: A hybrid SVM based decision tree. Pattern Recognit. 43(12), 3977–3987 (2010)Google Scholar
  2. 2.
    Somvanshi, M., Chavan, P.: A review of machine learning techniques using decision tree and support vector machine. In: IEEE ICCUBEA 2016. IEEE (2016)Google Scholar
  3. 3.
    SangitaB, P., Deshmukh, S.R.: Use of Support Vector Machine, decision tree, and Naive Bayesian techniques for wind speed classification. In: IEEE International Conference on Power and Energy Systems (ICPS) 2011. IEEE (2011)Google Scholar
  4. 4.
    Ponni, J., Shunmuganathan, K.L.: Multi-agent System for data classification from data mining using SVM. In: IEEE International Conference on Green Computing, Communication, and Conservation of Energy (ICGCE). IEEE (2016)Google Scholar
  5. 5.
    Pachghare, V.K., KulKarni, P.: Pattern-based network security using decision trees and support vector machine. In: IEEE International Conference on Electronics Computer Technology (ICECT) 2011. IEEE (2011)Google Scholar
  6. 6.
    Yasodha, S., Prakash, P.S.: Data mining classification for talent management using SVM. In: IEEE International Conference on Computing, Electronics and Electrical Technologies (ICCEET) 2012. IEEE (2012)Google Scholar
  7. 7.
    Zhao, H., Yao, Y., Liu, Z.: A classification method based on non-linear SVM decision tree. In: IEEE Fourth International Conference on Fuzzy Systems and Knowledge Discovery(FSKD) 2007. IEEE (2007)Google Scholar
  8. 8.
    Thamilselvan, P., Sathiaseelan, J.G.R.: Image classification using hybrid data algorithms–a review. In: IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) 2015. IEEE (2015)Google Scholar
  9. 9.
    Song, J., Guo, C., Wang, Z., Zhang, Y., Yu, G., Pierson, J.-M.: HaoLap: a hadoop based OLAP system for big data. J. Syst. Softw. 102, 167–181 (2015)Google Scholar
  10. 10.
    Agarwal, S., Yadav, L., Mehta, S.: Cricket team prediction with hadoop: statistical modeling approach. Proc. Comput. Sci. 122, 525–532 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • M. Karpagam
    • 1
  • S. Kanthimathi
    • 1
  • S. Akila
    • 1
  • S. Kanimozhi Suguna
    • 1
    Email author
  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia

Personalised recommendations