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.
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References
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 257–260
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
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.
Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool Publishers, pp 18–19, 27–28, 44–45, 47, 90–101
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 36–44
Chandrasekar S, Charon E, Ginet A (2012) CS229 project predicting the US presidential election using twitter data. In: CS229 machine learning course at Stanford University
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
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/
Buche A, Chandak MB, Zadgaonkar A (2013) Opinion mining and analysis: a survey. Int J Nat Lang Comput 39–48
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 41–45
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 1–15
Jhaveri D, Chaudhari A, Kurup L (2011) Twitter sentiment analysis on e-commerce websites in India. Int J Comput Appl (0975–8887) 127(18):14–18
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Jain, V.K., Virmani, D., Arora, P., Arora, A. (2018). “BOMEST” a Vital Approach to Extract the Propitious Information from the Big Data. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-3932-4_46
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DOI: https://doi.org/10.1007/978-981-10-3932-4_46
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