Advertisement

A Voting-Based Sentiment Classification Model

  • Dhara MungraEmail author
  • Anjali Agrawal
  • Ankit Thakkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 989)

Abstract

Sentiment analysis is used to depict sentiments present in the text structures, including news, reviews, and articles, and classify them as positive, or negative. It has gained significant attention due to the increase in individuals utilizing social media platforms to express sentiments about organizations, products, and administrations. Many methods are being devised to improve the efficacy of automated sentiment classification. The study proposes a voting-based ensemble model Majority Voting (MV) using five supervised machine learning classifiers named Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF) as base classifiers and a majority voting rule-based mechanism to get the final prediction. The performance of the proposed method is assessed using minimum, maximum, mean, and median values of precision, recall, f-score, and accuracy. The results of 900 values of the classification accuracy (3 datasets * 6 (classification methods) * 10 data subsets (k-fold cross-validation for \(k=10\)) * 5 runs), indicates that the proposed approach outperforms the individual classifiers in majority of the cases.

Keywords

Sentiment analysis Preprocessing Binary class Majority voting Different domains 

References

  1. 1.
    Angiani, G., Ferrari, L., Fontanini, T., Fornacciari, P., Iotti, E., Magliani, F., Manicardi, S.: A comparison between preprocessing techniques for sentiment analysis in twitter. In: KDWeb (2016)Google Scholar
  2. 2.
    Aziz, A.A., Starkey, A., Bannerman, M.C.: Evaluating cross domain sentiment analysis using supervised machine learning techniques. In: Intelligent Systems Conference (IntelliSys), 2017. pp. 689–696. IEEE (2017)Google Scholar
  3. 3.
    Borra, S., Ciaccio, A.D.: Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Comput. Stat. Data Anal. 54(12), 2976–2989 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Castro, D.W., Souza, E., Vitório, D., Santos, D., Oliveira, A.L.: Smoothed n-gram based models for tweet language identification: a case study of the brazilian and european portuguese national varieties. Appl. Soft Comput. 61, 1160–1172 (2017)CrossRefGoogle Scholar
  5. 5.
    Jha, V., Savitha, R., Shenoy, P.D., Venugopal, K., Sangaiah, A.K.: A novel sentiment aware dictionary for multi-domain sentiment classification. Comput. Electr. Eng. (2017)Google Scholar
  6. 6.
    Kotzias, D., Denil, M., De Freitas, N., Smyth, P.: From group to individual labels using deep features. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 597–606. ACM (2015)Google Scholar
  7. 7.
    Liu, Y., Bi, J.W., Fan, Z.P.: Multi-class sentiment classification: the experimental comparisons of feature selection and machine learning algorithms. Expert. Syst. Appl. 80, 323–339 (2017)CrossRefGoogle Scholar
  8. 8.
    Moraes, R., Valiati, J.F., Neto, W.P.G.: Document-level sentiment classification: an empirical comparison between svm and ann. Expert. Syst. Appl. 40(2), 621–633 (2013)CrossRefGoogle Scholar
  9. 9.
    Ngai, E., Lee, M., Choi, Y., Chai, P.: Multiple-domain sentiment classification for cantonese using a combined approach (2018)Google Scholar
  10. 10.
    Paliwal, P., Kumar, D.: Abc based neural network approach for churn prediction in telecommunication sector. In: International Conference on Information and Communication Technology for Intelligent Systems, pp. 343–349. Springer (2017)Google Scholar
  11. 11.
    Symeonidis, S., Effrosynidis, D., Arampatzis, A.: A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert. Syst. Appl. (2018)Google Scholar
  12. 12.
    Tellez, E.S., Miranda-Jiménez, S., Graff, M., Moctezuma, D., Siordia, O.S., Villaseñor, E.A.: A case study of spanish text transformations for twitter sentiment analysis. Expert. Syst. Appl. 81, 457–471 (2017)CrossRefGoogle Scholar
  13. 13.
    Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A., Spasić, I.: The role of idioms in sentiment analysis. Expert. Syst. Appl. 42(21), 7375–7385 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Institute of TechnologyNirma UniversityAhmedabadIndia

Personalised recommendations