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FDSS: Fuzzy Based Decision Support System for Aspect Based Sentiment Analysis in Big Data

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Advances in Computing and Data Sciences (ICACDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 906))

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Abstract

Sentiment analysis (SA) is an pioneering computing technology to advance the decision making process. Social media is used to bind the people by sharing their opinions. Opinion is in the free form of reviews or comments on micro-blogging sites such as online discussion forums, Twitter, Facebook and other different types of social networking sites. Fuzzy logic is one of the multi-valued logic. It considers the reasoning which is closer to the actual in lieu with fixed and exact. This research integrates fuzzy system to make business decisions in the proposed Fuzzy based Decision Support System (FDSS). The FDSS system uses the results of the analysis of the online sentiments and makes decisions based on some fuzzy rules. The fuzzy rules are defined to aid in the decision making process by classifying the sentiments of the aspects of a product. The Twitter mobile product dataset is used for experimental analysis and results show that the proposed FDSS system produces better results for decision making.

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References

  1. Molodtsov, D.: Soft set theory—first results. Comput. Math. Appl. 37(4–5), 19–31 (1999)

    Article  MathSciNet  Google Scholar 

  2. Maji, P.K., Biswas, R., Roy, A.R.: Intuitionistic fuzzy soft sets. J. Fuzzy Math. 9(3), 677–692 (2001)

    MathSciNet  MATH  Google Scholar 

  3. Bing, L., Chan, K.C.: A fuzzy logic approach for opinion mining on large scale twitter data. In: IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), pp. 652–657 (2014)

    Google Scholar 

  4. Nielsen, F.A.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs, pp. 1–6 (2011)

    Google Scholar 

  5. Suresh, H.: An unsupervised fuzzy clustering method for twitter sentiment analysis. In: IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp. 80–85 (2016)

    Google Scholar 

  6. Indhuja, K., Reghu, R.P.: Fuzzy logic based sentiment analysis of product review documents. In: IEEE First International Conference on Computational Systems and Communications (ICCSC), pp. 18–22 (2014)

    Google Scholar 

  7. Reshma, V., John, A.: Aspect based summarization of reviews using naïve Bayesian classifier and fuzzy logic. In: IEEE International Conference on Control Communication and Computing India (ICCC), pp. 617–621 (2015)

    Google Scholar 

  8. Supriya, B.N., Kallimani, V., Prakash, S., Akki, C.B.: Twitter sentiment analysis using binary classification technique. In: Vinh, P.C., Barolli, L. (eds.) ICTCC 2016. LNICST, vol. 168, pp. 391–396. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46909-6_36

    Chapter  Google Scholar 

  9. Dragoni, M., Petrucci, G.: A fuzzy-based strategy for multi-domain sentiment analysis. Int. J. Approx. Reason. 93, 59–73 (2018)

    Article  MathSciNet  Google Scholar 

  10. Wu, K.Y., Zhou, M.C., Lu, X.S., Huang, L.: A fuzzy logic-based text classification method for social media data. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1942–1947 (2017)

    Google Scholar 

  11. Anuradha, G., Joel, D.J.: Fuzzy based summarization of product reviews for better analysis. Indian J. Sci. Technol. 9(31), 1–9 (2016)

    Article  Google Scholar 

  12. Afzaal, M., Usman, M., Fong, A.C.M., Fong, S., Zhuang, Y.: Fuzzy aspect based opinion classification system for mining tourist reviews. Adv. Fuzzy Syst. 2016, 1–14 (2016)

    Article  Google Scholar 

  13. Jefferson, C., Liu, H., Cocea, M.: Fuzzy approach for sentiment analysis. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2017)

    Google Scholar 

  14. Mary, A.J.J., Arockiam, L.: ASFuL: aspect based sentiment summarization using fuzzy logic. In: IEEE International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), pp. 1–5 (2017)

    Google Scholar 

  15. Mary, A.J.J., Arockiam, L.: Aspect based parsing for sentiment analysis in big data. J. Comput. Sci. Mob. Appl. 5(12), 1–7 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India

    A. Jenifer Jothi Mary & L. Arockiam

Authors
  1. A. Jenifer Jothi Mary
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  2. L. Arockiam
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Corresponding author

Correspondence to A. Jenifer Jothi Mary .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague 8, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, Canada

    Tuncer Ören

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Mary, A.J.J., Arockiam, L. (2018). FDSS: Fuzzy Based Decision Support System for Aspect Based Sentiment Analysis in Big Data. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_8

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  • DOI: https://doi.org/10.1007/978-981-13-1813-9_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1812-2

  • Online ISBN: 978-981-13-1813-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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