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An Approach to Sentiment Analysis on Unstructured Data in Big Data Environment

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Smart Trends in Information Technology and Computer Communications (SmartCom 2016)

Abstract

An enormous growth of the WWW has been instrumental in spreading social networks. Due to many-fold increase in internet users taking to online reviews and opinions, the communication, sharing and collaboration through social networks have gained importance. The rapid growth in web-based activities has led to generation of huge amount of unstructured data which accounts for over 80% of the information. Exploiting big data alternatives in storing, processing, archiving and analyzing this data becomes increasingly necessary.

In this paper we propose a generalized approach to analyzing sentiments in big-data environment. The proposed model would serve to incorporate different supervised and un-supervised approaches to extraction, classification and scoring of opinions and sentiment words.

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References

  1. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35, 137–144 (2015). Elsevier

    Article  Google Scholar 

  2. Bravo-Marquez, F., Mendoza, M., Poblete, B.: Meta-level sentiment models for big social data analysis. Knowledge-Based Systems (2014)

    Google Scholar 

  3. Mukherjee, D., Krishnamurthy, K.: Maximizing the returns of big data. Cognizanti – Fact-based Insights, vol. 5, issue 1 (2012)

    Google Scholar 

  4. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  5. Liu, B.: Sentiment analysis: a multi-faceted problem. IEEE Intell. Syst. 25(3), 76–80 (2010)

    Article  Google Scholar 

  6. Banea, C., Mihalcea, R., Wiebe, J.: Multilingual sentiment and subjectivity analysis. Multilingual Natural Language Processing (2011)

    Google Scholar 

  7. Somasundaran, S.: Discourse-level relations for opinion analysis. PhD Dissertation Report, University of Pittsburgh (2010)

    Google Scholar 

  8. Neviarouskaya, A., Prendinger, H., Ishizuka, M.: SentiFul: a lexicon for sentiment analysis. IEEE Trans. Affect. Comput. 2(1), 22–36 (2011)

    Article  Google Scholar 

  9. Chen, H.: Trends and Controversies – AI and opinion mining part-2. In: IEEE Intelligent Systems (2010)

    Google Scholar 

  10. Paltoglou, G., Theunis, M., Kappas, A., Thelwall, M.: Predicting emotional responses to long informal text. IEEE Trans. Affect. Comput. 4(1), 267–279 (2013)

    Article  Google Scholar 

  11. Scagliarini, L.: Big Data, Unstructured Information Analysis is More Than Sentiment – a blog. Expert System – Semantic Intelligence

    Google Scholar 

  12. Chen, C., Chen, Z., Wu, C.: An unsupervised approach for person name bipolarization using principal component analysis. IEEE Trans. Knowl. Data Eng. 24(11), 1963–1976 (2012)

    Article  Google Scholar 

  13. Khabia, A., Chandak, M.: A cluster based approach with n-grams at word level for document classification. Int. J. Comput. Appl. 117(23), 38–42 (2015)

    Google Scholar 

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Correspondence to Dilipkumar A. Borikar .

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© 2016 Springer Nature Singapore Pte Ltd.

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Borikar, D.A., Chandak, M.B. (2016). An Approach to Sentiment Analysis on Unstructured Data in Big Data Environment. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_21

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  • DOI: https://doi.org/10.1007/978-981-10-3433-6_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3432-9

  • Online ISBN: 978-981-10-3433-6

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