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“BOMEST” a Vital Approach to Extract the Propitious Information from the Big Data

  • V. K. Jain
  • Deepali Virmani
  • Preeti Arora
  • Ankit Arora
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 9)

Abstract

Cost-effective, innovational methods to extract information and to provide analytic solutions for intensify perspicacity is one of the biggest challenge of big data. So an effective system to extract the accurate information is required for big data. Thus, in this paper, an efficient system to extract the propitious information from the raw big data (BOMEST) is proposed. Proposed BOMEST works by taking the raw data, preprocesses it, and extracts the accurate information based on polarity assigned using POS tagging. BOMEST is applied on the twitter dataset. BOMEST algorithm enhances and improves the accuracy of results by 78% as compared to existing lexicon approach.

Keywords

Big Data Twitter Rest API Sentiments Bag-of-words POS NLP and polarity 

References

  1. 1.
    Boden C, Karnstedt M, Fernandez M, Markl V (2013) Large-scale social-media analytics on stratosphere. In: Proceedings of the 22nd international conference on world wide web companion, pp 257–260Google Scholar
  2. 2.
    Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data (Springer). http://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0015-2
  3. 3.
    Virmani D, Taneja S, Bhatia P (2015) Maestro algorithm for sentiment evaluation. ACM, pp 244–249. doi:http://dx.doi.org/10.1145/2791405.2791479. ISBN 978-1-4503-3361-0/15/08.
  4. 4.
    Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool Publishers, pp 18–19, 27–28, 44–45, 47, 90–101Google Scholar
  5. 5.
    Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics: posters, pp 36–44Google Scholar
  6. 6.
    Chandrasekar S, Charon E, Ginet A (2012) CS229 project predicting the US presidential election using twitter data. In: CS229 machine learning course at Stanford UniversityGoogle Scholar
  7. 7.
    Kouloumpis E, Wilson T, Moore J (2011) Association for the advancement of artificial intelligence (http://www.aaai.org). In: Proceedings of the fifth international AAAI conference on weblogs and social media, pp 538–541
  8. 8.
    SentiWordNet: http://sentiwordnet.isti.cnr.it, WordNet website: http://wordnet.princeton.edu/,SentiWordNet is distributed under the Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license. http://creativecommons.org/licenses/by-sa/3.0/
  9. 9.
    Buche A, Chandak MB, Zadgaonkar A (2013) Opinion mining and analysis: a survey. Int J Nat Lang Comput 39–48Google Scholar
  10. 10.
    Nielsen FÅ (2011) A new: evaluation of a word list for sentiment analysis in Microblogs. In: Proceedings of the ESWC workshop on making sense of Microposts, pp 41–45Google Scholar
  11. 11.
    Bifet A, Frank E (2010) Sentiment knowledge discovery in twitter streaming data. In: Proceedings of 13th international conference on discovery science. Streaming twitter data, pp 1–15Google Scholar
  12. 12.
    Jhaveri D, Chaudhari A, Kurup L (2011) Twitter sentiment analysis on e-commerce websites in India. Int J Comput Appl (0975–8887) 127(18):14–18Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • V. K. Jain
    • 1
  • Deepali Virmani
    • 2
  • Preeti Arora
    • 2
  • Ankit Arora
    • 3
  1. 1.COER School of ManagementRoorkeeIndia
  2. 2.Bhagwan Parshuram Institute of TechnologyNew DelhiIndia
  3. 3.Mindfire SolutionsNoidaIndia

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