Access the full text.
Sign up today, get DeepDyve free for 14 days.
C. Pazzanese (2015)
Go ahead, be sarcastic | Harvard gazette
L. Lagerwerf (2007)
Irony and sarcasm in advertisements: Effects of relevant inappropriateness☆Journal of Pragmatics, 39
B. Altinel, M. Ganiz (2016)
A new hybrid semi-supervised algorithm for text classification with class-based semanticsKnowl. Based Syst., 108
Swati Verma, A. Ramamurthy (2016)
Analysis of Users' Comments on Political Portal for Extraction of Suggestions and Opinion MiningProceedings of the International Conference on Advances in Information Communication Technology & Computing
S. Piao, Paul Rayson, D. Archer, F. Bianchi, C. Dayrell, Mahmoud El-Haj, Ricardo-María Jiménez, Dawn Knight, M. Křen, Laura Löfberg, R. Nawab, Jawad Shafi, P. Teh, O. Mudraya (2016)
Lexical Coverage Evaluation of Large-scale Multilingual Semantic Lexicons for Twelve Languages
J. Creswell, V. Clark (2006)
Designing and Conducting Mixed Methods Research
Patricia Rockwell (2007)
Vocal Features of Conversational Sarcasm: A Comparison of MethodsJournal of Psycholinguistic Research, 36
P. Teh, Paul Rayson, Irina Pak, S. Piao, Seow Yeng (2016)
Reversing the Polarity with Emoticons
S. Attardo, Jodi Eisterhold, J. Hay, I. Poggi (2003)
Multimodal markers of irony and sarcasmHumor: International Journal of Humor Research, 16
Stefano Calzavara, R. Focardi, M. Squarcina, M. Tempesta (2017)
Surviving the WebACM Computing Surveys (CSUR), 50
Christine Liebrecht, Florian Kunneman, Antal Bosch (2013)
The perfect solution for detecting sarcasm in tweets #not
Cynthia Hee, Els Lefever, B. Verhoeven, Julie Mennes, Bart Desmet, G. Pauw, Walter Daelemans, Veronique Hoste (2015)
Automatic Detection and Prevention of Cyberbullying
Edwin Lunando, A. Purwarianti (2013)
Indonesian social media sentiment analysis with sarcasm detection2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS)
Florian Kunneman, Christine Liebrecht, M. Mulken, Antal Bosch (2015)
Signaling sarcasm: From hyperbole to hashtagInf. Process. Manag., 51
Ludovico Boratto, S. Carta, G. Fenu, Roberto Saia (2016)
Using neural word embeddings to model user behavior and detect user segmentsKnowl. Based Syst., 108
Li Huang, F. Gino, A. Galinsky (2015)
The highest form of intelligence: Sarcasm increases creativity for both expressers and recipientsOrganizational Behavior and Human Decision Processes, 131
S. McDonald (1999)
Exploring the Process of Inference Generation in Sarcasm: A Review of Normal and Clinical StudiesBrain and Language, 68
Hassan Saif, Yulan He, Harith Alani (2012)
Semantic Sentiment Analysis of Twitter
Hitoshi Uchiyama, D. Saito, H. Tanabe, T. Harada, A. Seki, K. Ohno, T. Koeda, N. Sadato (2012)
Distinction between the literal and intended meanings of sentences: A functional magnetic resonance imaging study of metaphor and sarcasmCortex, 48
Colin Sharp (2003)
Qualitative Research and Evaluation Methods (3rd ed.)Evaluation Journal of Australasia, 3
D. Maynard, M. Greenwood (2014)
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.
J. Jorgensen (1996)
The functions of sarcastic irony in speechJournal of Pragmatics, 26
M. Patton (1990)
Qualitative evaluation and research methods, 2nd ed.
Antonio Reyes, Paolo Rosso, D. Buscaldi (2012)
From humor recognition to irony detection: The figurative language of social mediaData Knowl. Eng., 74
R. Justo, T. Corcoran, S. Lukin, M. Walker, M. Torres (2014)
Extracting relevant knowledge for the detection of sarcasm and nastiness in the social webKnowl. Based Syst., 69
Ashwin Rajadesingan, R. Zafarani, Huan Liu (2015)
Sarcasm Detection on Twitter: A Behavioral Modeling ApproachProceedings of the Eighth ACM International Conference on Web Search and Data Mining
A. Pandey, D. Rajpoot, M. Saraswat (2017)
Twitter sentiment analysis using hybrid cuckoo search methodInf. Process. Manag., 53
Aditya Joshi, P. Bhattacharyya, Mark Carman (2016)
Automatic Sarcasm DetectionACM Computing Surveys (CSUR), 50
Minqing Hu, Bing Liu (2004)
Mining and summarizing customer reviewsProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Basilis Charalampakis, Dimitris Spathis, Elias Kouslis, Katia Kermanidis (2016)
A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweetsEng. Appl. Artif. Intell., 51
P. Teh, Paul Rayson, Irina Pak, S. Piao (2015)
Exploring fine-grained sentiment values in online product reviews2015 IEEE Conference on Open Systems (ICOS)
B. Pang, Lillian Lee (2008)
Opinion Mining and Sentiment AnalysisFound. Trends Inf. Retr., 2
Aditya Joshi, P. Bhattacharyya, Mark Carman (2016)
Automatic Sarcasm Detection: A SurveyarXiv: Computation and Language
P. Teh, Paul Rayson, Irina Pak, S. Piao (2015)
Sentiment analysis tools should take account of the number of exclamation marks!!!Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services
C. Burgers, Margot Mulken, P. Schellens (2011)
Finding Irony: An Introduction of the Verbal Irony Procedure (VIP)Metaphor and Symbol, 26
L. Phillips, R. Allen, R. Bull, A. Hering, M. Kliegel, S. Channon (2015)
Older adults have difficulty in decoding sarcasm.Developmental psychology, 51 12
D. Archer (2015)
Slurs, insults, (backhanded) compliments and other strategic facework movesLanguage Sciences, 52
C. Burgers, Margot Mulken, Peter Schellens (2012)
Verbal IronyJournal of Language and Social Psychology, 31
E. Cambria, Björn Schuller, Yunqing Xia, B. White (2016)
New avenues in knowledge bases for natural language processingKnowl. Based Syst., 108
Tiago Almeida, Tiago Silva, I. Santos, J. Hidalgo (2016)
Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filteringKnowl. Based Syst., 108
K. Rankin, Andrea Salazar, M. Gorno-Tempini, M. Sollberger, Stephen Wilson, Danijela Pavlic, C. Stanley, Shenly Glenn, M. Weiner, B. Miller (2009)
Detecting sarcasm from paralinguistic cues: Anatomic and cognitive correlates in neurodegenerative diseaseNeuroImage, 47
S. Agrawal, A. Awekar (2018)
Deep learning for detecting cyberbullying across multiple social media platforms
S. Dews, E. Winner (1995)
Muting the Meaning A Social Function of IronyMetaphor and Symbol, 10
(2017)
In Merriam-Webster.com
Elena Filatova (2012)
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing
Marta Dynel (2014)
Isn't it ironic? Defining the scope of humorous ironyHUMOR, 27
Lucas Sterckx, T. Demeester, Johannes Deleu, Chris Develder (2015)
Knowledge base population using semantic label propagationKnowl. Based Syst., 108
R. Bakshi, N. Kaur, R. Kaur, Gurpreet Kaur (2016)
Opinion mining and sentiment analysis2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)
R. Schifanella, Paloma Juan, J. Tetreault, Liangliang Cao (2016)
Detecting Sarcasm in Multimodal Social PlatformsProceedings of the 24th ACM international conference on Multimedia
Sarcasm is often used in everyday speech and writing and is prevalent in online contexts. The purpose of this paper is to investigate the analogy between sarcasm comments from sentiment tools and the human coder.Design/methodology/approachUsing the Verbal Irony Procedure, eight human coders were engaged to analyse comments collected from an online commercial page, and a dissimilarity analysis was conducted with sentiment tools. Three constants were tested, namely, polarity from sentiment tools, polarity rating by human coders; and sarcasm-level ratings by human coders.FindingsResults found an inconsistent ratio between these three constants. Sentiment tools used did not have the capability or reliability to detect the subtle, contextualized meanings of sarcasm statements that human coders could detect. Further research is required to refine the sentiment tools to enhance their sensitivity and capability.Practical implicationsWith these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – for example, to incorporate sophisticated human sarcasm texts in their analytical systems. Sarcasm exists frequently in media, politics and human forms of communications in society. Therefore, more highly sophisticated sentiment tools with the abilities to detect human sarcasm would be vital in research and industry.Social implicationsThe findings suggest that presently, of the sentiment tools investigated, most are still unable to pick up subtle contexts within the text which can reverse or change the message that the writer intends to send to his/her receiver. Hence, the use of the relevant hashtags (e.g. #sarcasm; #irony) are of fundamental importance in detection tools. This would aid the evaluation of product reviews online for commercial usage.Originality/valueThe value of this study lies in its original, empirical findings on the inconsistencies between sentiment tools and human coders in sarcasm detection. The current study proves these inconsistencies are detected between human and sentiment tools in social media texts and points to the inadequacies of current sentiment tools. With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – to incorporate sophisticated human sarcasm texts in their analytical systems. The system can then be used as a reference for psychologists, media analysts, researchers and speech writers to detect cues in the inconsistencies in behaviour and language.
Journal of Systems and Information Technology – Emerald Publishing
Published: Nov 14, 2018
Keywords: Detection; Social media; Linguistic; Sarcasm; Verbal irony
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.