Details

., Leveraging Machine Learning Tools and Techniques in the Detection of Parody on Twitter and Other Social Networking Sites

Saatvik Wadhwa

India

49-53

Vol: 9, Issue: 4, 2019

Receiving Date: 2019-09-09 Acceptance Date:

2019-11-20

Publication Date:

2019-12-11

Download PDF

Abstract

Parody is an inconspicuous type of contradiction, which can be broadly utilized informal communities like Twitter. It is normally used to send stowed away data, a message sent by individuals. Because of alternate purposes, I can utilize sarcasm like analysis and criticism. In any case, even this is hard for an individual to perceive. The snide redesign framework is extremely useful for improving programmed feeling investigation gathered from various interpersonal organizations and microblogging locales. Feeling examination alludes to web clients of a specific local area, communicated perspectives and assessments of ID and conglomeration. To distinguishing mockery, we propose an example-based methodology utilizing Twitter information. We present four arrangements of elements that incorporate a ton of explicit mockery. We use them to group tweets as mocking and non-snide. We likewise concentrate on every one of the proposed include sets and assess its extra expense groupings.www.replicasuizosdelujo.com

Keywords: parody; social networking sites; machine learning

References

  1. Yi Tay, Mondher Bouazizi And Tomoaki Otsuki (Ohtsuki), (24 August 2016), A Pattern-Based Approach For Sarcasm Detection On Twitter
  2. Yi Tay, Luu Anh Tuan, Siu Cheung Hui, JianSu, (8 May 2018), Reasoning with Sarcasm by Reading In-between”arXiv:1805.02856v1 [cs.CL]
  3. Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman. (2018), Sarcasm Detection Using Incongruity within Target Text; In: Investigations in Computational Sarcasm; Cognitive Systems Monographs, vol 37. Springer
  4. Shubhadeep Mukherjee, Dr. Pradip Kumar Bala, (2017), Sarcasm Detection in Microblogs Using Naïve Bayes and Fuzzy Clustering; In Proceedings of Technology in Society, pages 19-27
  5. Nishant Nikhil, Muktabh Mayank Srivastava, (2018), Binarizer at SemEval-2018 Task 3:Parsing dependency and deep learning for irony detection; arXiv preprintarXiv:1805.01112
  6. B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas, (July 2010), Short text classification in twitter to improve information filtering; in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retr.
  7. C. G. Akcora, M. A. Bayir, M. Demirbas, and H. Ferhatosmanoglu, (July 2010), Identifying breakpoints in public opinion; in Proc. 1st Workshop Soc. Media Anal.replicas relojes
  8. M. W. Berry, Ed., (2004), Survey of Text Mining: Clustering, Classification, and Retrieval. New York, NY, USA: Springer-Verlag
  9. B. Pang, L. Lillian, and V. Shivakumar, (Jul 2002), Thumbs up: Sentiment classification using machine learning techniques; in Proc. ACL Conf. Empirical Methods Natural Lang. Process., vol. 10.
  10. M. Boia, B. Faltings, C.-C. Musat, and P. Pu, (September 2013), A Is worth a thousand words: How people attach sentiment to emoticons and words in tweets,; in Proc. Int. Conf. Soc. Comput.
  11. K. Manuel, K. V.Indukuri, and P. R. Krishna, (December 2010), Analyzing internet slang for sentiment mining; in Proc. 2nd Vaagdevi Int. Conf. Inform. Technol. Real World Problems
  12. L. Dong, F.Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, (June 2014), Adaptive recursive neural network for target-dependent Twitter sentiment classification; in Proc. 52nd Annu. Meeting Assoc. Comput. Linguistics, vol. 2.
  13. F. Stringfellow, Jr., (1994), The Meaning of Irony: A Psychoanalytic Investigation. New York, NY, USA: State Univ. New York
  14. F. Stringfellow, Jr., (1994), The Meaning of Irony: A Psychoanalytic Investigation. New York, NY, USA: State Univ. New York
  15. C. Burfoot and T. Baldwin, (August 2009); Automatic satire detection: Are you having a laugh?’ in Proc. ACL-IJCNLP
  16. J. D. Campbell and A. N. Katz, (August 2012), Are there necessary conditions for inducing a sense of sarcastic irony?; Discourse Process. vol. 49, no. 6.
  17. J. Tepperman, D.Traum, and S. S. Narayanan (September 2006), Yeah right: Sarcasm recognition for spoken dialogue systems; in Proc. InterSpeech.
  18. T. Veale and Y. Hao (August 2010), Detecting ironic intent in creative comparisons; in Proc. ECAI.
  19. D. Ghosh, W. Guo, and S. Muresan, (September 2015), Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words; in Proc. EMNLP.
  20. Z. Wang, Z. Wu, R. Wang, and Y. Ren, (November 2015), Twitter sarcasm detection exploiting a context-based model; in Proc. Web Inf. Syst. Eng. (WISE)
  21. O. Tsur, D. Davidov, and A. Rappoport, (May 2010), ICWSM-A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews; in Proc. AAAI Conf. Weblogs Soc. Media.
  22. D. Davidov, O. Tsur, and A. Rappoport (July 2010), Semi-supervised recognition of sarcastic sentences in Twitter and Amazon; in Proc. 14th Conf. Comput. Natural Lang. Learn..
  23. D. Maynard and M. A. Greenwood, (May 2014), Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis; in Proc. 9th Int. Conf. Lang. Resour. Eval.
  24. E. Riloff, A. Qadir, P. Surve, L. De Silva, N. Gilbert, and R. Huang, (Oct 2013), Sarcasm as contrast between a positive sentiment and negative situation; in Proc. Conf. Empirical Methods Natural Lang. Process.
  25. A. Rajadesingan, R. Zafarani, and H. Liu, (February 2015), Sarcasm detection on Twitter: A behavioral modeling approach; in Proc. 18th ACM Int. Conf. Web Search Data Mining
  26. S. Muresan, R. Gonzalez-Ibanez, D. Ghosh, and N. Wacholder, (January 2016), Identification of nonliteral language in social media: A case study on sarcasm; J. Assoc. Inf. Sci. Technol.
Back

Disclaimer: All papers published in IJRST will be indexed on Google Search Engine as per their policy.

We are one of the best in the field of watches and we take care of the needs of our customers and produce replica watches of very good quality as per their demands.