Performance and Evaluation of Different Kernels in Support Vector Machine for Text Mining

  • Ashish Kumar Mourya
  • ShafqatUlAhsaan
  • Harleen Kaur
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


Text mining is the subfield of data mining. Text analysis or mining is enormously growing field for research simultaneously to artificial intelligence and data mining. Unstructured data or multimedia data is being constantly generated via social media Web sites, call center logs, blogs, and so on. Therefore, the explosion of textual data in a social media network is overwhelming. Text analysis or mining is widely being used to determine meaningful and noteworthy information for the huge amount of unstructured multimedia or heterogeneous data. In this paper, we compare the performance of text classification through support vector machine using multimedia data. This text analysis uses the technique of different fields like predict, machine learning, information, visualization, and natural language processing. The use of support vector machine for learning text nonlinear classifier is determined here. The reason for using SVM is that its performance is more accurate as compared to other soft computing tools and algorithms. In this paper, different kernel tricks have applied with nonlinear classifier for classification of text data mining. The results of proposed experiments predict that support vector machine with radial basis function achieves the highest overall accuracy.


Support vector machine (SVM) Corpus Kernel Text classification Decision tree Radial-based function (RBF) 


  1. 1.
    Kaushik A, Naithani S (2015) A study on sentiment analysis: methods and tools. Int J Sci Res 4(12). ISSN (Online): 2319-7064Google Scholar
  2. 2.
    Gupta V, Lehal GS A survey of text mining techniques and applications. J EmergGoogle Scholar
  3. 3.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New YorkzbMATHGoogle Scholar
  4. 4.
    Bishop CM (2006) Pattern recognition and machine learning. Springer, New YorkzbMATHGoogle Scholar
  5. 5.
    Marsland S (2009) Machine learning. An algorithmic perspective. Chapman and Hall/CRC Press, Boca RatonGoogle Scholar
  6. 6.
    Mohri A, Rostamizadeh A, Talwalker A (2012) Foundations of machine learning. The MIT Press, CambridgezbMATHGoogle Scholar
  7. 7.
    Tapia E, Bulacio P, Angelone L (2012) Sparse and stable gene selection with consensus SVM-RFE. Pattern Recogn Lett 33(2):164–172CrossRefGoogle Scholar
  8. 8.
    Ruiz R, Riquelme JC, Aguilar-Ruiz JS (2006) Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn 39(12):2383–2392CrossRefGoogle Scholar
  9. 9.
    Huerta R, Vembu S, Muezzinoglu MK, Vergara A (2012) Dynamical svm for time series classification. Pattern recognition. Springer, Berlin, Germany, pp 216–225Google Scholar
  10. 10.
    Fatima S, Srinivasu B (2017) Text document categorization using support vector machine. Int Res J Eng Technol (IRJET) 4(2):141–147Google Scholar
  11. 11.
    Hui JLO, Hoon GK, Zainon WMNW (2017) Effects of word class and text position in sentiment-based news classification. Procedia Comput Sci 124:77–85CrossRefGoogle Scholar
  12. 12.
    Jadon E, Sharma R (2017) Data mining: document classification using Naive Bayes classifier. Int J Comput Appl 167(6):13–16Google Scholar
  13. 13.
    Devika MD, Sunitha C, Ganesh A (2016) Sentiment analysis: a comparative study on different approaches. Procedia Comput Sci 87:44–49CrossRefGoogle Scholar
  14. 14.
    Arundthati A (2017) Assessment of decision tree algorithm on student’s recital. Int Res J Eng Technol (IRJET) 4Google Scholar
  15. 15.
    Purohit A, Atre D, Jaswani P, Asawara P (2015) Text classification in data mining. Int J Sci Res Publ 5(6):1–7Google Scholar
  16. 16.
    Tilve AKS, Jain SN (2017) A survey on machine learning techniques for text classification. Int J Eng Sci Res, TechnolGoogle Scholar
  17. 17.
    Kumar R, Verma R (2012) Classification algorithms for data mining: a survey. Int J Innovations Eng Technol (IJIET) 1(2):7–14Google Scholar
  18. 18.
    Vaghela VB, Jadav BM, Scholar ME (2016) Analysis of various sentiment classification techniques. Int J Comput Appl 140(3):0975–8887Google Scholar
  19. 19.
    Kharde V, Sonawane P (2016) Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971Google Scholar
  20. 20.
    Kalarikkal S, Remya PC (2015, June) Sentiment analysis and dataset collection: a comparative study. In: 2015 IEEE International Advance Computing Conference (IACC). IEEE, pp 519–524Google Scholar
  21. 21.
    Das TK, Acharjya DP, Patra MR (2014, January) Opinion mining about a product by analyzing public tweets in Twitter. In: 2014 international conference on computer communication and informatics. IEEE, pp 1–4Google Scholar
  22. 22.
    Shrivatava A, Mayor S, Pant B (2014) Opinion mining of real time twitter tweets. Int J Comput Appl 100(19)CrossRefGoogle Scholar
  23. 23.
    Chavan GS, Manjare S, Hegde P, Sankhe A (2014) A survey of various machine learning techniques for text classification. Int J Eng Trends Technol 15(6)Google Scholar
  24. 24.
    Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113CrossRefGoogle Scholar
  25. 25.
    Senthilkumar D, Paulraj S (2015, March) Prediction of low birth weight infants and its risk factors using data mining techniques. In: Proceedings of the 2015 international conference on industrial engineering and operations management, pp 186–194Google Scholar
  26. 26.
    Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633CrossRefGoogle Scholar
  27. 27.
    Kang H, Yoo SJ, Han D (2012) Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst Appl 39(5):6000–6010CrossRefGoogle Scholar
  28. 28.
    Abbasi A, Chen H, Salem A (2008) Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans Inf Syst (TOIS) 26(3):12CrossRefGoogle Scholar
  29. 29.
    Terachi M, Saga R, Tsuji H (2006, October) Trends recognition in journal papers by text mining. In 2006 IEEE international conference on systems, man and cybernetics, vol 6, pp 4784–4789Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ashish Kumar Mourya
    • 1
  • ShafqatUlAhsaan
    • 1
  • Harleen Kaur
    • 1
  1. 1.Department of Computer Science and EngineeringSchool of Engineering Sciences and Technology, JamiaHamdardNew DelhiIndia

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