Vol. 21 No. 3 (2022): Mapana Journal of Sciences
Research Articles

Drug Usage Analysis by VADER Sentiment Analysis on Leading Countries

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  • Sandipan Biswas,
  • Shivnath Ghosh
Sandipan Biswas
Brainware University, Kolkata.
Bio
Shivnath Ghosh
Brainware University, Kolkata.
Bio

Published 2022-12-06

Keywords

  • Clustering,
  • Opinion,
  • Sentiments,
  • Tweets,
  • VADER

Copyright (c) 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

In Twitter people from all part of the planet will build their opinions and take feed from the opinions which produces around five hundred million of tweets daily that amounts regarding 8TB of information. Data which are scrapped from Twitter may be helpful if analyzed as we are able to extract vital information via sentiment analysis. Opinions or comments regarding any news or launch of a product of quite trend may be ascertained well in twitter information. Our aim is to analysis of tweets on use of drugs for the treatment of COVID-19 diseases. In twitter sentiment analysis, we have a tendency to categorize those tweets into positive and negative sentiment and cluster along or a pack of cluster. We have conducted a study and locate that the application will quickly and efficiently distinguish numerous tweets on the idea of their sentiment scores and proportion and may notice weak and powerful positive or negative tweets once clustered with results of various dictionaries and establish a powerful support on our assumption. This paper surveys the polarity activity exploitation using VADER sentiment analysis on the utilization of drug for of COVID-19 treatment.

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References

  1. B.J. Jansen, M. Z hang, K. Sobeland and A. Chowdury, “Twitter power: Tweets as electronic word of mouth,” Journal of the American Society for Information Science and Technology, Inf. Sci. Technol., vol. 60, no. 11, pp. 2169–2188, 2009.
  2. V. Kharde and P. Sonawane., “Sentiment analysis of twitter data: a survey of techniques,” International Journal of Computer Applications, ArXiv1601.06971, 2016.
  3. P. Selvaperumal and A. Suruliandi, “A Short Message Classification Algorithm for Tweet Classification,” Int. Conf. Recent Trends Inf. Technol., pp. 1–3, 2014.
  4. T.Singh and M.Kumari , “Role of Text Pre-processing in Twitter Sentiment Analysis,” Procedia Comput. Sci., vol. 89, pp. 549–554, 2016.
  5. Y.R. Tausczik and J.W. Pennebaker, “The psychological meaning of words: LIWC and computerized text analysis methods,” J. Lang. Soc. Psychol., vol. 29, no. 1, pp. 24–54, 2010.
  6. C. J. H. E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” In Eighth International Conference on Weblogs and social media (ICWSM-14). 2016.
  7. D. Gurkhe, N. Pal and R. Bhatia., “Effective Sentiment Analysis of Social Media Datasets using Naïve Bayesian Classification,” International Journal of Computer Applications, 2014.
  8. M. Bouazizi and T. Ohtsuki, “Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer,” IEEE Access. 6, 64486-64502,2018.
  9. G. Gautam and D. Yadav, “Sentiment analysis of twitter data using machine learning approaches and semantic analysis,” Seventh International Conference on Contemporary Computing (IC3),2014.
  10. A. Amolik, N. Jivane, J. Mahavir Bhandari and M. Venkatesan, “Twitter sentiment analysis of movie reviews using machine learning techniques,” International Journal of Engineering and Technology 7.6: 1-7,2016.
  11. S. Mukherjee, A. Malu, A.R. Balamurali, and P. Bhattacharyya, “TwiSent: A Multistage System for Analyzing Sentiment in Twitter,” Proceedings of the 21st ACM international conference on Information and knowledge management,2013.
  12. D. Davidov, O. Tsur and A. Rappoport , “Enhanced Sentiment Learning Using Twitter Hashtags and Smileys,” Proceedings of the 23rd International Conference on Computational Linguistics: Posters, August 2010.
  13. M. Neethu and R. Rajasree, “Sentiment analysis in twitter using machine learning techniques,” Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT),2013.
  14. P. Garg, H. Garg, V. Ranga, “Sentiment Analysis of the Uri Terror Attack Using Twitter” International Conference on Computing, Communication and Automation (ICCCA2017),2017.
  15. A. Hasan, S. Moin, A. Karim and S. Shamshirb, “Machine Learning Based Sentiment Analysis for Twitter Accounts,” Licensee MDPI, Basel, Switzerland,2018.
  16. H.Bhavsar and R.Manglani, “Sentiment Analysis of Twitter Data using Python,” International Research Journal of Engineering and Technology (IRJET), 2019.
  17. S.Sirsat, S.Rao and B.Wukkadada, “Sentiment Analysis on Twitter Data for product evaluation,” IOSR Journal of Engineering (IOSRJEN),pp. 22-25,2019.
  18. S Behdenna , F Barigou and G Belalem, “Document Level Sentiment Analysis: A survey,”EAI Endorsed Transactions on Context-aware Systems and Applications, 2018.
  19. M.Taboada, J.Brooke, M.Tofiloski, K.Voll and M.Stede , “Lexicon-based methods for sentiment analysis,” Comput Linguist J .267–307,2011.
  20. R.M.Tong , “An operational system for detecting and tracking opinions in on-line discussions,” In: Working Notes of the SIGIR Workshop on Operational Text Classification, pp. 1–6 ,2001.
  21. P.Turney, and M.Littman , “Measuring praise and criticism: inference of semantic orientation from association,” ACM Transact Inform Syst J 21(4):315–346,2003.
  22. L. Kaur, “Review Paper on Twitter Sentiment Analysis Techniques,” In International Journal for Research in Applied Science & Engineering Technology (IJRASET) Volume 4 Issue X, October 2016 ISSN: 2321-9653,2016.
  23. G.A. Miller, R. Beckwith, C. Fellbaum, D. Gross and K.J. Miller, “Introduction to WordNet: An on-line lexical database,” International journal of lexicography, 3(4), pp-235-244,1990.
  24. S. Mohammad, C. Dunne, B. Dorr, “Generating high-coverage semantic orientation lexicons from overly marked words and a thesaurus,” In: Proceedings of the conference on Empirical Methods in Natural Language Processing, 2009.
  25. A. Harb, M. Plantie, G. Dray, M. Roche, F. Trousset and F. Poncelet, “Web opinion mining: How to extract opinions from blogs?” In Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST 08, pages 211–217,2008.
  26. P.D. Turney, “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews,” In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL’02, pages 417–424, 2002.
  27. G. Wang, and K. Araki, “Modifying SOPMI for Japanese weblog opinion mining by using a balancing factor and detecting neutral expressions,” In Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers, pages 189–192,2007.
  28. D.R. Rice and C. Zorn, “Corpus-based dictionaries for sentiment analysis of specialized vocabularies,” Proceedings of NDATAD, 98-115,2013.
  29. K.Y. Su, T.H. Chiang and J.S. Chang, “An Overview of Corpus-Based Statistics Oriented (CBSO) Techniques for Natural Language Processing,” Computational Linguistics and Chinese Language Processing 1(1), 101–157,1996.
  30. K.Y. Su and T.H. Chiang, “Some Key Issues in Designing MT Systems. Machine Translation,” Vol. 5, No. 4, pp. 265-300,1990.
  31. K.Y. Su and T.H. Chiang, “Why Corpus-Based Statistics Oriented Machine Translation,” Proceedings of 4th Int. Conf. on Theoretical and Methodological Issue in Machine Translation, pp. 249-262, Montreal, Canada, June 25-27, 1992.
  32. K.Y. Su, J.S. Chang, and Y.L. Una Hsu, “A Corpus-based Two-Way Design for Parameterized MT Systems: Rationale, Architecture and Training Issues,” Proceedings of the Sixth International Conference on Theoretical and Methodological Issues in Machine Translation, TMI-95, vol. 2, pp. 334-353, 1995.
  33. H.E. Moss, R.K. Ostrin, L.K. Tyler and W.D. Marslen, “Accessing Different Types of Lexical Se-mantic Information: Evidence from Priming,” Journal of Experimental Psychology: Learning, Memory and Cognition, 21:863-883(1995).
  34. Natural Language Toolkit http://www.nltk.org/ (Date Last Accessed, November 20, 2018.
  35. S. Bird, E. Loper and E. Klein, “Natural Language Processing with Python,” O’Reilly Media Inc,2009.
  36. C. J. H. E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” In Eighth International Conference on Weblogs and social media (ICWSM-14),2014.
  37. S. Elbagir and J. Yang, “Twitter Sentiment Analysis Using Natural Language Toolkit and VADER Sentiment,” In Lecture Notes in Engineering and Computer Science: Proceedings of The International Multiconference of Engineers and Computer Scientists,2019.
  38. Natural Language Processing with neural networks http://nilc.icmc.usp.br/nlpnet/.
  39. E.R. Fonseca and J.L.G. Rosa, “A Two-Step Convolutional Neural Network Approach for Semantic Role Labeling,” Proceedings of the 2013 International Joint Conference on Neural Networks, 2013. pp. 2955-2961, 2013.
  40. E.R. Fonseca and J.L.G. Rosa, “Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging,” Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology. pp. 98-107,2013.
  41. S. A. El Rahman, F. A. AlOtaibi and W. A. AlShehri, "Sentiment Analysis of Twitter Data," 2019 International Conference on Computer and Information Sciences (ICCIS), 2019, pp. 1-4, doi: 10.1109/ICCISci.2019.8716464.

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