Recent Trends in Sarcasm Detection on Online Social Networks

Authors

  • K Ranganath Dept. of CSE, Sumathi Reddy Institute of Technology for Women, JNTU, Hyderabad, India
  • MD Sallauddin Dept. of CSE, SR Engineering Colleges, Hyderabad, India
  • Shabana Dept. of CSE, Sumathi Reddy Institute of Technology for Women, JNTU, Hyderabad, India

DOI:

https://doi.org/10.26438/ijcse/v5i10.235239

Keywords:

Sarcasm, Sarcasm detection, Twitter

Abstract

Online Social Networks become largest platform to express people feelings, opinions, views and real time events such as live tweets etc. Example Twitter has 315 million monthly active users, eighty two percent of active users on mobile and millions of tweets are being circulated through twitter every day. Various organizations as well as companies are interested in twitter data for finding the views of various people towards their products or events. Sarcasm refers to expressing negative feelings using positive words. To detect sarcasm among those tweets is comparatively more difficult. This paper discussed various approaches to find sarcasm on twitter. With the help of sarcasm detection, companies could analyze the feelings of user about their products. This is helpful for companies, as the companies could improve their quality of product.

References

Mukherjee S, Bala PK, “Sarcasm detection in microblogs using Naïve Bayes and fuzzy clustering “.Technology in Society, Volume 48,page no:19-27, February, 2017.

Shubhodip Saha, Jainath Yadav and Prabhat Ranjan ,”Proposed approach for sarcasm detection in twitter”, Indian Journal of Science and Technology, Vol 10, Issue 25, July 2017.

Ashwin Rajadesingan, Reza Zafarani, and Huan Liu , “Sarcasm detection on twitter: A behavioral model approach”, WSDM’15 Proceedings of 8th ACM International Conference on Web Search and Data Mining Conference,PP:97-106, February, 2015, Shanghai, China.

Erik Cambria1, Soujanya Poria, Federica Bisio, Rajiv Bajpai, and Iti Chaturvedi,”The CLSA Model: A novel framework for concept-level sentiment analysis”, Springer International Publishing, pp 3-22,2015.

Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, Sune Lehmann, “Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm”, Association for Computational Linguistics, pp 1616-1626, Oct 2017.

David Bamman and Noah A. Smith, “Contextualized Sarcasm Detection on Twitter”, Proceedings of the Ninth International AAAI Conference on Web and Social Media, UK, 2015.

, Li Huang , Francesca Gino , Adam D. Galinsky, “The highest form of intelligence: Sarcasm increases creativity for both expressers and recipientsOrganizational Behavior and Human Decision Processes”, Vol 131, pp 162–177, 2015.

Aditya Joshi, Pushpak Bha.acharyya, and Mark J Carman. “Automatic Sarcasm Detection: A Survey”, ACM Comput. Surv. 2017.

M.-S. Yang, “A survey of fuzzy clustering”, Math. Comput. Model. Vol 18, Issue 11, pp 1—16, 1993.

S. Chattopadhyay, D. Pratihar, S. Sarkar, ” A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms”, Comput. Informatics. Vol 30, pp 701–720, 2012.

Mukherjee, B. Liu, “Improving gender classification of blog authors”, EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Pages 207-217, 2010.

S. Argamon, M. Koppel, J.W. Pennebaker, J. Schler, “Mining the Blogosphere: Age, gender and the varieties of self-expression”, First Monday, Vol 12, 2007

Tayal DK, Yadav S, Gupta K, Rajput B. “Polarity detection of sarcastic political tweets”, Computing for Sustainable Global Development (INDIACom), 2014 International Conference, pp 605-628, 2014.

Utsumi A.Verbal , “irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony”, Journal of Pragmatics, Vol 32, Issue 12, pp 1777-1806, 2000.

Filatova E, “Irony and sarcasm: Corpus generation and analysis using crowd sourcing”, LREC, European Language Resources Association, pp 392-398, 2012.

Downloads

Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i10.235239
Published: 2025-11-12

How to Cite

[1]
K. Ranganath, M. Sallauddin, and Shabana, “Recent Trends in Sarcasm Detection on Online Social Networks”, Int. J. Comp. Sci. Eng., vol. 5, no. 10, pp. 235–239, Nov. 2025.