An improved wrapper-based feature selection for efficient opinion mining

  • Authors

    • Prasanna Moorthi N
    • Mathivanan V
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.3.10659
  • Opinion Mining, Feature Extraction, Wrapper Based Feature Selection, Concept Based Feature Expansion

  • Abstract

    Opinion mining analyses people’s opinions, evaluations, sentiments, attitudes, appraisals and emotions to entities like products, organizations, services, issues, individuals, topics, events and their attributes. It is a large problem space having high feature dimensionality. Feature extraction is important in opinion mining as customers do not usually express product opinions totally, but separately based on individual features. Two tasks should be accomplished in feature-based opinion mining. First, product features on which reviewers expressed opinions must be identified and extracted. Second, opinion orientation or polarities must be determined. Finally, opinion mining summarizes extracted features and opinions. In this work a novel wrapper based feature selection mechanism using concept based feature expansion is proposed. The wrapper based technique uses the principles of evolutionary algorithms.

  • References

    1. [1] Agarwal, A., Xie, B., Vovsha, I., Rambow, O., &Passonneau, R. (2011, June). Sentiment analysis of twitter data. In Proceedings of the workshop on languages in social media (pp. 30-38). Association for Computational Linguistics.

      [2] Asghar, M. Z., Khan, A., Ahmad, S., &Kundi, F. M. (2014). A review of feature extraction in sentiment analysis. Journal of Basic and Applied Scientific Research, 4(3), 181-186.

      [3] Chandrashekar, G., &Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.

      [4] Tran, C. T., Zhang, M., Andreae, P., &Xue, B. (2016). Improving performance for classification with incomplete data using wrapper-based feature selection. Evolutionary Intelligence, 9(3), 81-94.

      [5] Buche, A., Chandak, D., &Zadgaonkar, A. (2013). Opinion mining and analysis: a survey. arXiv preprint arXiv:1307.3336.

      [6] Angiani, G., Ferrari, L., Fontanini, T., Fornacciari, P., Iotti, E., Magliani, F., &Manicardi, S. (2016). A Comparison between Preprocessing Techniques for Sentiment Analysis in Twitter. In KDWeb.

      [7] Samsudin, N., Puteh, M., Hamdan, A. R., &Nazri, M. Z. A. (2013, July). Immune based feature selection for opinion mining. In Proceedings of the World Congress on Engineering (Vol. 3, pp. 3-5).

      [8] Kumar, J. A., &Abirami, S. (2015). An Experimental Study Of Feature Extraction Techniques In Opinion Mining. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 4(1).

      [9] Schouten, K., &Frasincar, F. (2015, May). The benefit of concept-based features for sentiment analysis. In Semantic Web Evaluation Challenge (pp. 223-233). Springer, Cham.

      [10] Shang, L., Zhou, Z., & Liu, X. (2016). Particle swarm optimization-based feature selection in sentiment classification. Soft Computing, 20(10), 3821-3834.

      [11] He, R., &McAuley, J. (2016, April). Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web (pp. 507-517). International World Wide Web Conferences Steering Committee.

      [12] G.Vinodhini and RM.Chandrasekaran, Sentiment Analysis and Opinion Mining: A Survey, Volume 2, Issue 6, June 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering.

      [13] He, S. (2010). Training Artificial Neural Networks Using Lévy Group Search Optimizer. Multiple-Valued Logic and Soft Computing, 16(6), 527-545.

      [14] Qing Cao, Mark A. Thompson, Yang Yu, (2012), “Sentiment analysis in decision sciences research: An illustration to IT governance, Decision Support Systems, http://dx.doi.org/10.1016/j.dss.2012.10.026.

  • Downloads

  • How to Cite

    Moorthi N, P., & V, M. (2017). An improved wrapper-based feature selection for efficient opinion mining. International Journal of Engineering & Technology, 7(1.3), 140-145. https://doi.org/10.14419/ijet.v7i1.3.10659

    Received date: 2018-03-26

    Accepted date: 2018-03-26

    Published date: 2017-12-31