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Sentimental Analysis of Twitter Data on Hadoop

  • Jayanta Choudhury
  • Chetan Pandey
  • Anuj Saxena
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Data is something without which organizations can never reach any conclusion and cannot extract any particular pattern. These data sets are the sources on which organizations rely while taking important strategic decisions. There are many social platforms on which people around the world are accessing and these platforms are generating a huge amount of data. This data can be differentiated on the basis of their volume, velocity and variety. Organizations term such a huge amount of data as Big Data. These social data sets are of great use for improving business strategies. Nowadays, twitter has become a great social platform for expressing different opinions. This paper focuses on MapReduce-based sentiment analysis of data received through twitter. The data is first cleaned to retain only text, then MapReduce is applied to get the frequency of each word which is then matched with the dictionary created for positive and negative words over Hadoop environment. The results are compared with Naïve Bayes and SVM classifier. It has been observed that time consumed by the proposed system is 45% less than SVM and 38% less than Naïve Bayes. The accuracy in terms of a total number of words detected, positive and negative words, was also observed to be 11%, 16%, 18% respectively in case of SVM and 9%, 13%, 16% respectively in case of Naïve Bayes.

Keywords

Big data Hadoop MapReduce SVM Naïve Bayes 

References

  1. 1.
    Lam, C., Davis, M., Gaddam, A.: Hadoop in Action, 2nd edn. Manning Publications (2016)Google Scholar
  2. 2.
    Kenekar, T.V., Dani, A.R.: An efficient private FIM on hadoop MapReduce. In: IEEE International Conference on Automatic Control and Dynamic Optimization Techniques, Pune, India, Sept 9–10, 2017.  https://doi.org/10.1109/icacdot.2016.7877554
  3. 3.
    Kumar, S., Singh, P., Rani, S.: Sentimental analysis of social media using R language and Hadoop: Rhadoop. In: 5th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), Noida, India, Sept 7–9 (2016).  https://doi.org/10.1109/icrito.2016.7784953
  4. 4.
    Islam, M.R., Zibran, M.F.: A comparison of dictionary building methods for sentiment analysis in software engineering text. In: Empirical Software Engineering and Measurement, Toronto, ON, Canada, Nov 9–10 (2017).  https://doi.org/10.1109/esem.2017.67
  5. 5.
    Ameur, H., Jamoussi, S.: Dynamic construction of dictionaries for sentiment classification. In: 13th IEEE International Conference on Data Mining Workshop, Dallas, TX, USA, Dec 7–10 (2013).  https://doi.org/10.1109/icdmw.2013.34
  6. 6.
    Mallika, C., Selvamuthukumaran, S.: Hadoop framework: analyzes workload prediction of data from cloud computing. In: IEEE International Conference on IoT and Application, Nagapattinam, India, May 19–20 (2017).  https://doi.org/10.1109/iciota.2017.8073624
  7. 7.
    Ahmed, K., Tazi, N.E., Hossny, A.H.: Sentiment analysis over social networks: an overview. In: IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, China, Oct 9–12 (2015).  https://doi.org/10.1109/smc.2015.380
  8. 8.
    Nanli, Z., Ping, Z., Weiguo, L.: Sentiment analysis: a literature review. In: IEEE International Symposium on Management of Technology, Hangzhou, China, Nov 8–9 (2012).  https://doi.org/10.1109/ismot.2012.6679538
  9. 9.
    Povoda, L., Burget, R., Dutta, M.K.: Sentiment analysis based on support vector machine and big data. In: IEEE 39th International Conference on Telecommunications and Signal Processing, Vienna, Austria, June 27–29 (2016).  https://doi.org/10.1109/tsp.2016.7760939
  10. 10.
    Ahlgren, O.: Research on sentiment analysis: the first decade. In: 16th IEEE International Conference on Data Mining Workshops, Barcelona, Spain, Dec 12–15 (2016).  https://doi.org/10.1109/icdmw.2016.0131
  11. 11.
    Chandankhede, C., Devle, P., Waskar, A.: ISAR: Implicit sentiment analysis of user reviews. In: International Conference on Computing, Analytics and Security Trends, Puna, India, Dec 19–21 (2016).  https://doi.org/10.1109/cast.2016.7914994

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Graphic Era UniversityDehradunIndia
  2. 2.Institute de informaticaDehradunIndia

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