CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Opinion Mining of GST Implementation using Supervised Machine Learning Approach

by Nandini Tomar, Ritesh Srivastava, Bindiya Ahuja
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 49
Year of Publication: 2018
Authors: Nandini Tomar, Ritesh Srivastava, Bindiya Ahuja
10.5120/ijca2018917283

Nandini Tomar, Ritesh Srivastava, Bindiya Ahuja . Opinion Mining of GST Implementation using Supervised Machine Learning Approach. International Journal of Computer Applications. 180, 49 ( Jun 2018), 1-7. DOI=10.5120/ijca2018917283

@article{ 10.5120/ijca2018917283,
author = { Nandini Tomar, Ritesh Srivastava, Bindiya Ahuja },
title = { Opinion Mining of GST Implementation using Supervised Machine Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 180 },
number = { 49 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number49/29566-2018917283/ },
doi = { 10.5120/ijca2018917283 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:59.868375+05:30
%A Nandini Tomar
%A Ritesh Srivastava
%A Bindiya Ahuja
%T Opinion Mining of GST Implementation using Supervised Machine Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 49
%P 1-7
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment Analysis is a way to determine the emotions behind social media discussions. Analyzing social data plays a vital role in knowing people’s behavior about an entity or event occurring in the society. Sentiment analysis is widely used in a variety of applications like classifying, summarizing and aggregating reviews from the massive amount of unstructured data that may be available from customer comments, blogs, feedback and reviews on any product or social issue. The Goods & Service Tax(GST) was debated a lot in the social network as it impacts every citizen of India and there was a state of confusion among people about this amendment in the taxation system. If this state of people can be determined, then it can help in identifying how effectively GST scheme is executing. In this paper, we present Sentiment Analysis (SA) of GST by using the textual content of Twitter to determine the public opinion about GST.

References
  1. R. Srivastava, H. Kumar, M. Bhatia, and S. Jain, "Analyzing Delhi assembly election 2015 using textual content of social network," in Proceedings of the Sixth International Conference on Computer and Communication Technology 2015, 2015, pp. 78-85: ACM.
  2. K. Pabreja, "GST sentiment analysis using twitter data," IJAR, vol. 3, no. 7, pp. 660-662, 2017.
  3. Z. Z. Alp and Ş. G. Öğüdücü, "Identifying topical influencers on twitter based on user behavior and network topology," Knowledge-Based Systems, vol. 141, pp. 211-221, 2018.
  4. I. Anger and C. Kittl, "Measuring influence on Twitter," in Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, 2011, p. 31: ACM.
  5. D. A. Shamma, L. Kennedy, and E. F. Churchill, "Tweet the debates: understanding community annotation of uncollected sources," in Proceedings of the first SIGMM workshop on Social media, 2009, pp. 3-10: ACM.
  6. H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?," in Proceedings of the 19th international conference on World wide web, 2010, pp. 591-600: ACM.
  7. (27-04-2018). Goods and Services Tax (India). Available: https://en.wikipedia.org/wiki/Goods_and_Services_Tax_(India)
  8. V. Mittal and I. Kashyap, "Empirical Study of Impact of Various Concept Drifts in Data Stream Mining Methods," International Journal of Intelligent Systems and Applications, vol. 8, no. 12, p. 65, 2016.
  9. D. Mumtaz and B. Ahuja, "Sentiment analysis of movie review data using Senti-Lexicon Algorithm," in Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on, 2016, pp. 592-597: IEEE.
  10. R. Srivastava and M. Bhatia, "Offline vs. Online Sentiment Analysis: Issues With Sentiment Analysis of Online Micro-Texts," International Journal of Information Retrieval Research (IJIRR), vol. 7, no. 4, pp. 1-18, 2017.
  11. R. Srivastava, M. Bhatia, H. K. Srivastava, and C. Sahu, "Exploiting grammatical dependencies for fine-grained opinion mining," in Computer and communication technology (iccct), 2010 international conference on, 2010, pp. 768-775: IEEE.
  12. V. B. Vaghela and B. M. Jadav, "Analysis of various sentiment classification techniques," International Journal of Computer Applications, vol. 140, no. 3, 2016.
  13. D. Mumtaz and B. Ahuja, "A Lexical and Machine Learning-Based Hybrid System for Sentiment Analysis," in Innovations in Computational Intelligence: Springer, 2018, pp. 165-175.
  14. 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, 2002, pp. 417-424: Association for Computational Linguistics.
  15. B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends® in Information Retrieval, vol. 2, no. 1–2, pp. 1-135, 2008.
  16. T.-C. Peng and C.-C. Shih, "Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews," in Digital Information Management (ICDIM), 2010 Fifth International Conference on, 2010, pp. 120-127: IEEE.
  17. T. Zagibalov and J. Carroll, "Unsupervised classification of sentiment and objectivity in Chinese text," in Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I, 2008.
  18. R. Srivastava and M. Bhatia, "Quantifying modified opinion strength: A fuzzy inference system for sentiment analysis," in Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, 2013, pp. 1512-1519: IEEE.
  19. R. Srivastava and M. Bhatia, "Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event," International Journal of Information Technology and Web Engineering (IJITWE), vol. 12, no. 4, pp. 1-21, 2017.
  20. F. Sebastiani, "Machine learning in automated text categorization," ACM computing surveys (CSUR), vol. 34, no. 1, pp. 1-47, 2002.
  21. Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. "A practical guide to support vector classification." (2003): 1-16.
  22. Joachims, Thorsten. "Text categorization with support vector machines: Learning with many relevant features." European conference on machine learning. Springer, Berlin, Heidelberg, 1998.
  23. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, no. 1, pp. 10-18, 2009.
  24. Holmes, Geoffrey, Andrew Donkin, and Ian H. Witten. "Weka: A machine learning workbench." In Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on, pp. 357-361. IEEE, 1994.
  25. https://developer.twitter.com. (22-04-2018). Docs.
  26. Gentry, Jeff, Maintainer Jeff Gentry, Suggests RSQLite, and RMySQL License Artistic. "Package ‘twitteR’." R package version 1, no. 9 (2016).
  27. I. Feinerer, "Introduction to the tm Package Text Mining in R," ed, 2017.
  28. S. Baccianella, A. Esuli, and F. Sebastiani, "Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining," in LREC, 2010, vol. 10, no. 2010, pp. 2200-2204.
  29. B. Pang and L. Lee, "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts," in Proceedings of the 42nd annual meeting on Association for Computational Linguistics, 2004, p. 271: Association for Computational Linguistics.
  30. Forman, George. "An extensive empirical study of feature selection metrics for text classification." Journal of machine learning research 3, no. Mar (2003): 1289-1305.
  31. Accuracy, Precision, Recall & F1 Score: Interpretation Of .., http://blog.exsilio.com/all/accuracy-precision-recall-f1-score-interpretation-of (accessed June 04, 2018).
  32. R. Sharma, S. Nigam, and R. Jain, "Determination of Polarity of sentences using Sentiment Orientation System," International journal of Advances in Computer Science and Technology (IJACST) WARSE, vol. 3, no. 3, pp. 182-187, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis GST Supervised Machine Learning.

Powered by PhDFocusTM