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Cluster Computing

, Volume 22, Supplement 1, pp 1199–1209 | Cite as

Classification of sentence level sentiment analysis using cloud machine learning techniques

  • R. ArulmuruganEmail author
  • K. R. Sabarmathi
  • H. Anandakumar
Article

Abstract

Cloud machine learning (CML) techniques offer contemporary machine learning services, with pre-trained models and a service to generate own personalized models. This paper presents a completely unique emotional modeling methodology for incorporating human feeling into intelligent systems. The projected approach includes a technique to elicit emotion factors from users, a replacement illustration of emotions and a framework for predicting and pursuit user’s emotional mechanical phenomenon over time. The neural network based CML service has better training concert and enlarged exactness compare to other large scale deep learning systems. Opinions are important to almost all human activities and cloud based sentiment analysis is concerned with the automatic extraction of sentiment related information from text. With the rising popularity and availability of opinion rich resources such as personal blogs and online appraisal sites, new opportunities and issues arise as people now, actively use information technologies to explore and capture others opinions. In the existing system, a segmentation ranking model is designed to score the usefulness of a segmentation candidate for sentiment classification. A classification model is used for predicting the sentiment polarity of segmentation. The joint framework is trained directly using the sentences annotated with only sentiment polarity, without the use of any syntactic or sentiment annotations in segmentation level. However the existing system still has issue with classification accuracy results. To improve the classification performance, in the proposed system, cloud integrate the support vector machine, naive bayes and neural network algorithms along with joint segmentation approaches has been proposed to classify the very positive, positive, neutral, negative and very negative features more effectively using important feature selection. Also to handle the outliers we apply modified k-means clustering method on the given dataset. It is used to cloud cluster the outliers and hence the label as well as unlabeled features is handled efficiently. From the experimental result, we conclude that the proposed system yields better performance than the existing system.

Keywords

Cloud machine learning Sentiment analysis Segmentation Cloud clustering Classification 

References

  1. 1.
    Chan, M., Campo, E., Estève, D., Fourniols, J.-Y.: Smart homes-current features and future perspectives. Maturitas 64(2), 90–97 (2009)CrossRefGoogle Scholar
  2. 2.
    Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980). doi: 10.1037/h0077714 CrossRefGoogle Scholar
  3. 3.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  4. 4.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lectures Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  5. 5.
    Castro, F., Gelbukh, A., Mendoza, M.G.: An introduction to concept-level sentiment analysis. MICAI 8266, 478–483 (2013)Google Scholar
  6. 6.
    Suganya, M., Anandakumar, H.: Handover based spectrum allocation in cognitive radio networks. In: 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, pp. 215–219 (2013). doi: 10.1109/ICGCE.2013.6823431
  7. 7.
    Tang, D., Qin, B., Wei, F., Dong, L., Liu, T., Zhou, M.: A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans. Audio Speech Lang. Process. 23(11), 1750–1761 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhao, J., Dong, L., Wu, J., Xu, K.: Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In: Proceedings of SIGKDD (2012)Google Scholar
  9. 9.
    Tang, D., et al.: Coooolll: a deep learning system for twitter sentiment classification. In: Semantic Evaluation (SemEval 2014) (2014)Google Scholar
  10. 10.
    Havasi, C., Cambria, E., Schuller, B., Liu, B., Wang, H.: Knowledge-based approaches to concept-level sentiment analysis. IEEE Intell. Syst. 28(2), 0012–14 (2013)CrossRefGoogle Scholar
  11. 11.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  12. 12.
    Gendron, M., Barrett, L.F.: Reconstructing the past: a century of ideas about emotion in psychology. Emot. Rev. 1(4), 316–339 (2009). doi: 10.1177/1754073909338877 CrossRefGoogle Scholar
  13. 13.
    Anandakumar, H., Umamaheswari, K.: Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Clust. Comput. 1–11 (2017). doi: 10.1007/s10586-017-0798-3
  14. 14.
    Lindquist, K.A.: Emotions emerge from more basic psychological ingredients: a modern psychological constructionist model. Emot. Rev. 5(4), 356–368 (2013). doi: 10.1177/1754073913489750 CrossRefGoogle Scholar
  15. 15.
    Ekman, P., Friesen, W.V.: Unmasking the face: a guide to recognizing emotions from facial clues, 1968, Ishk (1975)Google Scholar
  16. 16.
    Tsai, J.L., Louie, J.Y., Chen, E.E., Uchida, Y.: Learning what feelings to desire: socialization of ideal affect through children’s storybooks. Pers. Soc. Psychol. Bull. 33(1), 17–30 (2007). doi: 10.1177/0146167206292749 CrossRefGoogle Scholar
  17. 17.
    Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009). doi: 10.1109/TPAMI.2008.52 CrossRefGoogle Scholar
  18. 18.
    Bi, C., Wang, H., Bao, R.: SAR image change detection using regularized dictionary learning and fuzzy clustering. In: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 327–330 (2014, November)Google Scholar
  19. 19.
    Turney, P. D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  20. 20.
    Maas, A.L., Daly, R.E., Pham, P.T. Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150 (2011)Google Scholar
  21. 21.
    Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1386–1395 (2010)Google Scholar
  22. 22.
    Choi, Y., Cardie, C.: Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 793–801 (2008)Google Scholar
  23. 23.
    Mohammad, S.M., Dorr, B.J., Hirst, G., Turney, P.D.: Computing lexical contrast. Comput. Linguist. 39(3), 555–590 (2013)CrossRefGoogle Scholar
  24. 24.
    Nalov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., Wilson, T.: Semeval-2013 task 2: Sentiment analysis in twitter. In: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 13, pp. 312–320 (2013)Google Scholar
  25. 25.
    Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, pp. 115–124 (2005)Google Scholar
  26. 26.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon based methods for sentiment analysis. Comput. linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  27. 27.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  28. 28.
    Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust. Comput. 20, 1517–1525 (2017)CrossRefGoogle Scholar
  29. 29.
    McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured models for fine-to-coarse sentiment analysis. In: Annual Meeting-Association for Computational Linguistics, vol. 45, p. 432 (2007)Google Scholar
  30. 30.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)Google Scholar
  31. 31.
    Wu, Z., Wang, H.: Super-resolution reconstruction of SAR image based on non-local means denoising combined with BP neural network. arXiv:1612.04755 (2016)
  32. 32.
    Wang, H., Wang, J.: An effective image representation method using Kernel classification. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence. IEEE, (2014, November) doi: 10.1109/ictai.2014.131
  33. 33.
    Chang, V., Kuo, Y.-H., Ramachandran, M.: Cloud computing adoption framework: a security framework for business clouds. Fut. Gener. Comput. Syst. 57, 24–41 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Information TechnologyBannari Amman Institute of TechnologySathyamangalamIndia
  2. 2.Department of Computer Science and EngineeringAkshaya College of Engineering and TechnologyCoimbatoreIndia

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