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Effective Emoticon Based Framework for Sentimental Analysis of Web Data

  • Shoieb AhamedEmail author
  • Ajit Danti
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job.

Keywords

Sentiment analysis Opinion mining Emoticon SentiWordNet 

References

  1. 1.
    Bouguelia, M.R., Nowaczyk, S., Santosh, K.C., et al.: Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int. J. Mach. Learn. Cybern. 9, 1307 (2018).  https://doi.org/10.1007/s13042-017-0645-0CrossRefGoogle Scholar
  2. 2.
    Dang, Y., Zhang, Y., Chen, H.: A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. Intell. Syst. IEEE 25(4), 46–53 (2010)CrossRefGoogle Scholar
  3. 3.
    Virmani, D., Malhotra, V., Tyagi, R.: Sentimental analysis using collaborated opinion mining. Int. J. Soft Comput. Eng. 4(ICCIN-2014) (2014). ISSN 2331–2037Google Scholar
  4. 4.
    Liang, P.-W., Dai, B.-R.: Opinion mining on social media data. In: 14th International Conference on Mobile Data Management (MDM), vol. 2. IEEE (2013)Google Scholar
  5. 5.
    Li, L., et al.: Multi-domain active learning for text classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2012)Google Scholar
  6. 6.
    Li, G., Liu, F.: A clustering-based approach on sentiment analysis. In: International Conference on IEEE Intelligent Systems and Knowledge Engineering (ISKE) (2010)Google Scholar
  7. 7.
    Ohana, B., Tierney, B.: Sentiment classification of reviews using SentiWordNet. In: 9th IT&T Conference. Dublin Institute of Technology, Dublin, Ireland, p. 13 (2009)Google Scholar
  8. 8.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  9. 9.
    Vaidya, S., Rafi, M.: An improved SentiWordNet for opinion mining and sentiment analysis. J. Adv. Database Manag. Syst. 1(2), 1–7 (2014)Google Scholar
  10. 10.
    Vajda, S., Santosh, K.C.: A fast k-nearest neighbor classifier using unsupervised clustering. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 185–193. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-4859-3_17CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceGovernment First Grade CollegeSoraba, ShimogaIndia
  2. 2.Department of Computer Science and EngineeringChrist(Deemed to be University)BangaloreIndia

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