SARCASMET: EXTENSION OF LEXICON ALGORITHM FOR EMOJI-BASED SARCASM DETECTION FROM TWITTER DATA
DOI:
https://doi.org/10.46243/jst.2024.v9.i1.pp76-88Keywords:
Emoji-Based Sarcasm, Lexicon Algorithm, Twitter Data, SarcasmetAbstract
Since the industrial revolution, the original way of communicating; face-to-face communication has been used as a model to develop the various ways of communicating known to date. Transposing the principles and codes of natural face-to-face communication to today’s online communication is a major challenge for developers. In today's digital era, social media platforms like Twitter have become a hub for expressing opinions, emotions, and humor. Sarcasm, a form of verbal irony, is a prevalent means of communication on such platforms. However, detecting sarcasm in online content poses a significant challenge due to the absence of vocal intonations and facial expressions. This necessitates the development of reliable methods to automatically identify and understand sarcasm in tweets. Sarcasm makes use of positive lingual contents to convey a negative message. Different types of approaches have been developed to implement sarcasm detection on online communication platforms. However, the levels of efficiency of these approaches have been the principal worries of developers. Lexicon algorithm is used to determine the sentiment expressed by a textual content. This sentiment might be negative, neutral, or positive. It is possible to be sarcastic using only positive or neutral sentiment textual contents. Hence, lexicon algorithm can be useful but insufficient for sarcasm detection. It is necessary to extend the lexicon algorithm to come up with systems that would be proven efficient for sarcasm detection on neutral and positive sentiment textual contents. In this work, two sarcasm analysis systems both obtained from the extension of the lexicon algorithm have been proposed for that sake. The first system consists of the combination of a lexicon algorithm and a pure sarcasm analysis algorithm. The second system consists of the combination of a lexicon algorithm and a sentiment prediction algorithm. Finally, the proposed model aims to detect the sarcasm from the text and emotion icon with improved efficiency.
References
Edwards, V.V. Sarcasm: What It Is and Why It Hurts Us. 2014. Available online: https:// www.scienceofpeople.com/sarcasm-why-ithurts-us/ (accessed on 5 October 2021)
Rothermich, K.; Ogunlana, A.; Jaworska, N. Change in humor and sarcasm use based on anxiety and depression symptom severity during the COVID-19 pandemic. J. Psychiatr. Res. 2021, 140, 95–100
Ezaiza, H.; Humayoun, S.R.; Al Tarawneh, R.; Ebert, A. Person-vis: Visualizing personal social networks (ego networks). In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, USA, 7–12 May 2016; pp. 1222–1228
Akula, R.; Garibay, I. Viztract: Visualization of complex social networks for easy user perception. Big Data Cogn. Comput. 2019, 3, 17
Singh, B.; Sharma, D.K. Predicting image credibility in fake news over social media using multi-modal approach. Neural Comput. Appl. 2021, 34, 21503–21517
Singh, B.; Sharma, D.K. SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural network. Comput. Ind. Eng. 2021, 162, 107733
Khodak, M.; Saunshi, N.; Vodrahalli, K. A large self-annotated corpus for sarcasm. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC), Miyazaki, Japan, 7–12 May 2018
Eke, C.I.; Norman, A.A.; Shuib, L.; Nweke, H.F. Sarcasm identification in textual data: Systematic review, research challenges and open directions. Artif. Intell. Rev. 2020, 53, 4215–4258
Sarsam, S.M.; Al-Samarraie, H.; Alzahrani, A.I.; Wright, B. Sarcasm detection using machine learning algorithms in Twitter: A systematic review. Int. J. Mark. Res. 2020, 62, 578–598
Keerthi Kumar, H.M.; Harish, B.S. Sarcasm classification: A novel approach by using Content Based Feature Selection Method. Procedia Comput. Sci. 2018, 143, 378–386
Pawar, N.; Bhingarkar, S. Machine Learning based Sarcasm Detection on Twitter Data. In Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 10–12 June 2020
Ghosh, A.; Veale, T. Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 7–11 September 2017; pp. 482–491
Ghosh, D.; Fabbri, A.S.; Muresan, S. Sarcasm analysis using conversation context. Comput. Linguist. 2018, 44, 755–792
Liu, L.; Priestley, J.L.; Zhou, Y.; Ray, H.E.; Han, M. A2text-net: A novel deep neural network for sarcasm detection. In Proceedings of the IEEE First International Conference on Cognitive Machine Intelligence (CogMI), Los Angeles, CA, USA, 12–14 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 118–126
Published
Versions
- 2025-03-27 (2)
- 2024-01-25 (1)
How to Cite
Issue
Section
