, 44:6 | Cite as

Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews

  • Rashmi K ThakurEmail author
  • Manojkumar V Deshpande


Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, self-adaptive lion algorithm. In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, Naive Bayes, neural network, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249%, specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611%, respectively, for train review and movie review databases.


Sentiment classification SVM MapReduce LA sentiwordnet 


  1. 1.
    Lin Y, Wan H, Jiang R, Wu Z and Jia X 2015 Inferring the travel purposes of passenger groups for better understanding of passengers. IEEE Trans. Intell. Transp. Syst. 16(1): 235–243CrossRefGoogle Scholar
  2. 2.
    Hurk E, Kroon L, Maróti G and Vervest P 2015 Deduction of passengers’ route choices from smart card data. IEEE Trans. Intell. Transp. Syst. 16(1): 430–440CrossRefGoogle Scholar
  3. 3.
    Ali F, Kim EK and Kim Y G 2015 Fuzzy ontology-based opinion mining and information extraction: a proposal to automate the hotel reservation system. Appl. Intell. 42(3): 481–500CrossRefGoogle Scholar
  4. 4.
    Ali D, Kwak P, Khan S M R, Islam K H, Kim and Kwak K S 2017 Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling. Transp. Res. Part C: Emerg. Technol. 77: 33–48CrossRefGoogle Scholar
  5. 5.
    Havasi C, Cambria E, Schuller B, Liu B and Wang H 2013 Knowledge- based approaches to concept-level sentiment analysis. IEEE Intell. Syst. 28(2): 0012–14CrossRefGoogle Scholar
  6. 6.
    Manning C D and Schütze H 1999 Foundations of statistical natural language processing, Cambridge, MA: MIT PresszbMATHGoogle Scholar
  7. 7.
    Pang B, Lee L and Vaithyanathan S 2002 Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the EMNLP, pp. 79–86Google Scholar
  8. 8.
    Tang D, Qin B, Wei F, Dong L, Liu T and Zhou M 2015 A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans. Audio, Speech Lang. Process. 23(11): 1750–1761CrossRefGoogle Scholar
  9. 9.
    Quan C and Ren F 2014 Unsupervised product feature extraction for feature-oriented opinion determination. Inf. Sci. 272: 16–28CrossRefGoogle Scholar
  10. 10.
    Catal C and Nangir M 2017 A sentiment classification model based on multiple classifiers. Appl. Soft Comput. 50: 135–141CrossRefGoogle Scholar
  11. 11.
    Xia R, Zong C and Li S 2011 Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6): 1138–1152CrossRefGoogle Scholar
  12. 12.
    Liu B 2012 Sentiment analysis and opinion mining. San Rafael: Morgan & ClaypoolGoogle Scholar
  13. 13.
    Phu V N, Dat N D, Tran V T N and Chau T A, Nguyen 2016 fuzzy C-means for English sentiment classification in a distributed system. Appl. Intell. pp. 1–22Google Scholar
  14. 14.
    Yang Y and Pedersen J O 1997 A comparative study on feature selection in text categorization. In: Proceedings of the ICML’97, pp. 412–420Google Scholar
  15. 15.
    Li J, Fong S, Zhuang Y and Khoury R 2016 Hierarchical classification in text mining for sentiment analysis of online news. Soft Comput. 20(9): 3411–3420CrossRefGoogle Scholar
  16. 16.
    Taboada M, Brooke J, Tofiloski M, Voll K and Stede M 2011 Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2): 267–307CrossRefGoogle Scholar
  17. 17.
    Zhao J, Dong L, Wu J and Xu K 2012 Moodlens: An emoticon-based sentiment analysis system for chinese tweets. In: Proceedings of the SIGKDD Google Scholar
  18. 18.
    Maas A L, Daly R E, Pham P T, Huang D, Ng AY and Potts C 2011 Learning word vectors for sentiment analysis. In: Proceedings of the ACL Google Scholar
  19. 19.
    Hung C and Lin H 2013 Using objective words in SentiWordNet to improve word-of-mouth sentiment classification. IEEE Intell. Syst. 28(2): 47–54CrossRefGoogle Scholar
  20. 20.
    Salehan M and Kim D J 2016 Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decis. Support Syst. 81; 30–40CrossRefGoogle Scholar
  21. 21.
    Chen T, Xu R, He Y and Wang X 2017 Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst. Appl. 72: 221–230CrossRefGoogle Scholar
  22. 22.
    Saif, Hassan, Fernández, Miriam, He, Yulan and Alani, Harith 2014 On stopwords, filtering and data sparsity for sentiment analysis of Twitter. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation, pp. 810–817Google Scholar
  23. 23.
    Esuli A and Sebastiani F 2006 SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06), 417-422Google Scholar
  24. 24.
    Ohana B and Tierney B 2009 Sentiment classification of reviews using SentiWordNet. In: 9th IT&T Conference, Dublin Institute of Technology, Dublin, Ireland Google Scholar
  25. 25.
    Boser B E, Guyon I M and Vladimir N Vapnik 1992 A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on COMPUTATIONAL Learning Theory COLT ‘92, pp. 144–152Google Scholar
  26. 26.
    Rajakumar B R 2014 Lion algorithm for standard and large scale bilinear system identification: A global optimization based on Lion’s social behavior. In: 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, pp. 2116–2123Google Scholar
  27. 27.
  28. 28.

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Mukesh Patel School of Technology Management & Engineering, Narsee Monjee Institute of Management StudiesMumbaiIndia
  2. 2.Prestige Institute of Engineering Management and ResearchIndoreIndia

Personalised recommendations