Sarcasm, a sharp and ironic utterance designed to cut or to cause pain, is often used to express strong emotions, such as contempt, mockery or bitterness. Sarcasm detection is of great importance in understanding people’s true sentiments and opinions. Most Existing techniques of detecting sarcasm use speech, writing text, signs as a base to catch percent of sarcasm even with limited accuracy. In this article, a new approach of detecting sarcasm using three techniques of machine learning has been proposed. Eight action units and eight head movements from Facial Action Coding System are considered as a base rule to generate effective features to train and test machine learning techniques. The selection of these units and movements are performed according the advice of some specialist psychiatrists. The simulation results view significant accuracy of diagnosing sarcasm exceed 86% which reflects the robustness of the proposed approach also open the way for further works in this field of research.
REFERENCES
1.
Ekman
, P.
, et al., Final report to NSF of the planning workshop on facial expression understanding.
Human Interaction Laboratory, University of California, San Francisco
, 1993
. 378
.2.
Ekman
, P.
, J.C.
Hager
, and W.V.
Friesen
, The symmetry of emotional and deliberate facial actions.
Psychophysiology
, 1981
. 18
(2
): p. 101
–106
.3.
Bartlett
, M.S.
, et al., Classifying facial action.
Advances in neural information processing systems
, 1996
: p. 823
–829
.4.
Farha
, I.A.
and W.
Magdy
. From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
. in Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection.
2020
.5.
Liu
, B.
, Sentiment analysis and opinion mining.
Synthesis lectures on human language technologies
, 2012
. 5
(1
): p. 1
–167
.6.
Pang
, B.
, L.
Lee
, and S.
Vaithyanathan
, Thumbs up? Sentiment classification using machine learning techniques.
arXiv preprint cs/0205070, 2002
.7.
Abdul-Mageed
, M.
, M.
Diab
, and M.
Korayem
. Subjectivity and sentiment analysis of modern standard Arabic
. in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
. 2011
.8.
Abdelali
, A.
, J.
Cowie
, and H.S.
Soliman
. Arabic information retrieval perspectives
. in Proceedings of the 11th Conference on Natural Language Processing, Journes d’Etude sur la Parole- Traitement Automatique des Langues Naturelles (JEP-TALN)
. 2004
.9.
Habash
, N.Y.
, Introduction to Arabic natural language processing.
Synthesis Lectures on Human Language Technologies
, 2010
. 3
(1
): p. 1
–187
.10.
Bouazizi
, M.
and T.O.
Ohtsuki
, A pattern-based approach for sarcasm detection on twitter.
IEEE Access
, 2016
. 4
: p. 5477
–5488
.11.
Abercrombie
, G.
and D.
Hovy
. Putting sarcasm detection into context: The effects of class imbalance and manual labelling on supervised machine classification of twitter conversations
. in Proceedings of the ACL 2016 student research workshop.
2016
.12.
Barbieri
, F.
, F.
Ronzano
, and H.
Saggion
. Italian irony detection in twitter: a first approach
. in The First Italian Conference on Computational Linguistics CLiC-it.
2014
.13.
Barbieri
, F.
, H.
Saggion
, and F.
Ronzano
. Modelling sarcasm in twitter, a novel approach
. in Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis.
2014
.14.
Filatova
, E.
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing
. in Lrec.
2012
. Citeseer.15.
Ghosh
, D.
, W.
Guo
, and S.
Muresan
. Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words
. in proceedings of the 2015 conference on empirical methods in natural language processing.
2015
.16.
Joshi
, A.
, et al. Harnessing sequence labeling for sarcasm detection in dialogue from tv series ‘friends’
. in Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning.
2016
.17.
Rajadesingan
, A.
, R.
Zafarani
, and H.
Liu
. Sarcasm detection on twitter: A behavioral modeling approach
. in Proceedings of the eighth ACM international conference on web search and data mining.
2015
.18.
Joshi
, A.
, V.
Sharma
, and P.
Bhattacharyya
. Harnessing context incongruity for sarcasm detection
. in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
. 2015
.19.
Amir
, S.
, et al., Modelling context with user embeddings for sarcasm detection in social media.
arXiv preprint arXiv:1607.00976, 2016
.20.
Karoui
, J.
, F.B.
Zitoune
, and V.
Moriceau
, Soukhria: Towards an irony detection system for arabic in social media.
Procedia Computer Science
, 2017
. 117
: p. 161
–168
.21.
Ghanem
, B.
, et al. Idat at fire2019: Overview of the track on irony detection in arabic tweets
. in Proceedings of the 11th Forum for Information Retrieval Evaluation
. 2019
.22.
Abbes
, I.
, et al. Daict: A dialectal arabic irony corpus extracted from twitter
. in Proceedings of The 12th Language Resources and Evaluation Conference
. 2020
.23.
Oster
, H.
and D.
Rosenstein
, Baby FACS: Analyzing facial movement in infants.
Unpublished manuscript, New York University
, 1993
.
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