Abstract
For a long time, figurative language was studied merely from linguistic perspectives, yet it has lately captured the attention of other fields, such as natural language processing, sentiment analysis, and machine learning. The increasing interest in figurative language calls for a clear overview of figurative language research. To address this need, we present a review of English literature on figurative language applied to social networks in a five-year period: from 2013 to 2017. The aim of this review is to identify the most commonly researched figurative devices, as well as their discriminant features, detection approaches and methods, and languages in which they are studied. To this end, we analyze and evaluate 521 research works and present 45 primary studies. The results show that sarcasm is the most studied figurative device, with 56% of the total frequencies. Also, 87% of the studies are based on the supervised machine learning approach, and the support vector machine classifier has been the most used to detect the different types of figurative language (i.e., figurative devices). Similarly, more than half of the literature focuses on figurative language in English.
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Reyes Pérez A (2012) Linguistic-based patterns for figurative language processing: the case of humor recognition and irony detection. Dissertation, Universitat Politècnica de València
Khokhlova M, Patti V, Rosso P (2016) Distinguishing between irony and sarcasm in social media texts: Linguistic observations. In: International FRUCT conference on intelligence, social media and web (ISMW FRUCT). IEEE, pp 1–6
Campbell JD, Katz AN (2012) Are there necessary conditions for inducing a sense of sarcastic irony? Discourse Process 49:459–480. https://doi.org/10.1080/0163853X.2012.687863
Camp E (2012) Sarcasm, pretense, and the semantics/pragmatics distinction. Noûs 46:587–634. https://doi.org/10.1111/j.1468-0068.2010.00822.x
Eisterhold J, Attardo S, Boxer D (2006) Reactions to irony in discourse: evidence for the least disruption principle. J Pragmat 38:1239–1256. https://doi.org/10.1016/j.pragma.2004.12.003
Bosco C, Patti V, Bolioli A (2013) Developing corpora for sentiment analysis: the case of irony and senti-TUT. IEEE Intell Syst 28:55–63. https://doi.org/10.1109/MIS.2013.28
Sulis E, Irazú Hernández Farías D, Rosso P, Patti V, Ruffo G (2016) Figurative messages and affect in Twitter: differences between #irony, #sarcasm and #not. Knowl Based Syst 108:132–143. https://doi.org/10.1016/j.knosys.2016.05.035
Ghosh A, Li G, Veale T, Shutova E (2015) Semeval-2015 task 11: sentiment analysis of figurative language in twitter. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 470–478
Reyes A, Rosso P (2012) Making objective decisions from subjective data: detecting irony in customer reviews. Decis Support Syst 53:754–760. https://doi.org/10.1016/J.DSS.2012.05.027
Reyes A, Rosso P, Buscaldi D (2012) From humor recognition to irony detection: the figurative language of social media. Data Knowl Eng 74:1–12. https://doi.org/10.1016/j.datak.2012.02.005
Benamara F, Grouin C, Karoui J (2017) Analyse d’opinion et langage figuratif dans des tweets : présentation et résultats du Défi Fouille de Textes DEFT2017. In: 24e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), pp 1–12
Berger M (1998) The nonuse of figurative language in conduct disordered adolescents. Utah State University, Logan
Glazkova A, Ganzherli N, Mikhalkova E (2019) UTMN at HAHA@IberLEF2019: recognizing humor in Spanish tweets using hard parameter sharing for neural networks. In: Iberian Languages Evaluation Forum (IberLEF 2019)
Recupero DR, Dragoni M, Buscaldi D, Alam M, Cambria E (2018) EMSASW2018. In: 4th Workshop on sentic computing, sentiment analysis, opinion mining, and emotion detection
Ortega. A, Lleida E, San Segundo R, Ferreiros J, Hurtado L, Sanchis E, Torres MI, Justo R (2018) AMIC: affective multimedia analytics with inclusive and natural communication. [Sociedad Española para el Procesamiento del Lenguaje Natural]
Reyes A (2013) Linguistic-based patterns for figurative language processing: the case of humor recognition and irony detection. Sociedad Española para el Procesamiento del Lenguaje Natural
Joshi A, Bhattacharyya P, Carman MJ (2016) Automatic sarcasm detection: a survey. ACM Comput Surv 10:1–8
Wallace BC (2015) Computational irony: a survey and new perspectives. Artif Intell Rev 43:467–483. https://doi.org/10.1007/s10462-012-9392-5
Wicana SG, Ibisoglu TY, Yavanoglu U (2017) A review on sarcasm detection from machine-learning perspective. In: IEEE 11th international conference on semantic computing (ICSC). IEEE, pp 469–476
Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 80:571–583. https://doi.org/10.1016/j.jss.2006.07.009
Figueroa C, Vagliano I, Rocha OR, Morisio M (2015) A systematic literature review of linked data-based recommender systems. Concurr Comput Pract Exp 27:4659–4684. https://doi.org/10.1002/cpe.3449
Wen J, Li S, Lin Z, Hu Y, Huang C (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54:41–59. https://doi.org/10.1016/J.INFSOF.2011.09.002
Liebrecht CC, Kunneman FA, Bosch APJ van den (2013) The perfect solution for detecting sarcasm in tweets #not. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. New Brunswick, NJ: ACL, Atlanta, Georgia, pp 29–37
Ghosh D, Guo W, Muresan S (2015) 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. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1003–1012
Khattri A, Joshi A, Bhattacharyya P, Carman M (2015) Your sentiment precedes you: using an author’s historical tweets to predict sarcasm. In: Proceedings of the 6th workshop on computational approaches to subjectivity, sentiment and social media analysis. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 25–30
Charalampakis B, Spathis D, Kouslis E, Kermanidis K (2015) Detecting irony on greek political tweets. In: Proceedings of the 16th international conference on engineering applications of neural networks (INNS)—EANN’15. ACM Press, New York, pp 1–5
Bouazizi M, Otsuki Ohtsuki T (2016) A pattern-based approach for sarcasm detection on twitter. IEEE Access 4:5477–5488. https://doi.org/10.1109/ACCESS.2016.2594194
Barberi F, Ronzano F, Saggion Horacio (2015) Do we criticise (and laugh) in the same way? automatic detection of multi-lingual satirical news in twitter. In: Proceedings of the 24th international conference on artificial intelligence. The Association for the Advancement of Artificial Intelligence Press, Buenos Aires, pp 1215–1221
Amir S, Wallace BC, Lyu H, Paula C, Silva MJ (2016) Modelling context with user embeddings for sarcasm detection in social media. In: Conference on computational natural language learning (CONLL 2016). pp 1–11
Sulis E, Irazú Hernández Farías D, Rosso P, Patti V, Ruffo G (2016) Figurative messages and affect in Twitter: differences between #irony, #sarcasm and #not. Knowl Based Syst 108:132–143. https://doi.org/10.1016/j.knosys.2016.05.035
Charalampakis B, Spathis D, Kouslis E, Kermanidis K (2016) A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets. Eng Appl Artif Intell 51:50–57. https://doi.org/10.1016/j.engappai.2016.01.007
Reyes A, Rosso P, Veale T (2013) A multidimensional approach for detecting irony in Twitter. Lang Resour Eval 47:239–268. https://doi.org/10.1007/s10579-012-9196-x
Hernańdez Farías DI, Patti V, Rosso P (2016) Irony detection in twitter: the role of affective content. ACM Trans Internet Technol 16:1–24. https://doi.org/10.1145/2930663
Nguyen HL, Jung JE (2017) Statistical approach for figurative sentiment analysis on social networking services: a case study on twitter. Multimed Tools Appl 76:8901–8914. https://doi.org/10.1007/s11042-016-3525-9
de Freitas LA, Vanin AA, Hogetop DN, Bochernitsan MN, Vieira R (2014) Pathways for irony detection in tweets. In: Proceedings of the 29th annual ACM symposium on applied computing—SAC’14. ACM Press, New York, pp 628–633
Fersini E, Pozzi FA, Messina E (2015) Detecting irony and sarcasm in microblogs: the role of expressive signals and ensemble classifiers. In: IEEE international conference on data science and advanced analytics (DSAA). IEEE, pp 1–8
Thu PP, New N (2017) Impact analysis of emotion in figurative language. In: IEEE/ACIS 16th international conference on computer and information science (ICIS). IEEE, pp 209–214
Ling J, Klinger R (2016) An empirical, quantitative analysis of the differences between sarcasm and irony. Springer, Cham, pp 203–216
Hernández-Farías I, Benedí J-M, Rosso P (2015) Applying basic features from sentiment analysis for automatic irony detection, pp 337–344
Jasso G, Meza I (2016) Character and word baselines systems for irony detection in Spanish short texts. Proces del Leng Nat 56:41–48
Barberi F, Saggion H (2014) Modelling irony in twitter: feature analysis and evaluation. In: LREC 2014: ninth international conference on language resources and evaluation. European Language Resources Association (ELRA), Reykjavik, pp 4258–4264
Barbieri F, Ronzano F, Saggion H (2014) Italian irony detection in twitter: a first approach. In: The first Italian conference on computational linguistics CLiC-it 2014 and the fourth international workshop EVALITA, pp 28–32
Barbieri F, Saggion H (2014) Automatic detection of irony and humour in twitter. In: ICCC, pp 155–162
Kunneman F, Liebrecht C, van Mulken M, van den Bosch A (2015) Signaling sarcasm: from hyperbole to hashtag. Inf Process Manag 51:500–509. https://doi.org/10.1016/j.ipm.2014.07.006
Mukherjee S, Bala PK (2017) Sarcasm detection in microblogs using Naïve Bayes and fuzzy clustering. Technol Soc 48:19–27. https://doi.org/10.1016/j.techsoc.2016.10.003
Muresan S, Gonzalez-Ibanez R, Ghosh D, Wacholder N (2016) Identification of nonliteral language in social media: a case study on sarcasm. J Assoc Inf Sci Technol 67:2725–2737. https://doi.org/10.1002/asi.23624
Bouazizi M, Ohtsuki T (2015) Opinion mining in twitter how to make use of sarcasm to enhance sentiment analysis. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015—ASONAM’15. ACM Press, New York, pp 1594–1597
Rajadesingan A, Zafarani R, Liu H (2015) Sarcasm detection on twitter. In: Proceedings of the eighth ACM international conference on web search and data mining—WSDM’15. ACM Press, New York, pp 97–106
Wang Z, Wu Z, Wang R, Ren Y (2015) Twitter sarcasm detection exploiting a context-based model. In: Proceedings, part I, of the 16th international conference on web information systems engineering—WISE 2015, vol 9418. Springer, New York, pp 77–91
Bouazizi M, Ohtsuki T (2015) Sarcasm detection in twitter: “all your products are incredibly amazing!!!”—Are they really? In: IEEE global communications conference (GLOBECOM). IEEE, pp 1–6
Lunando E, Purwarianti A (2013) Indonesian social media sentiment analysis with sarcasm detection. In: International conference on advanced computer science and information systems (ICACSIS). IEEE, pp 195–198
Dave AD, Desai NP (2016) A comprehensive study of classification techniques for sarcasm detection on textual data. In: International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, pp 1985–1991
Tayal DK, Yadav S, Gupta K, Rajput B, Kumari K (2014) Polarity detection of sarcastic political tweets. In: International conference on computing for sustainable global development (INDIACom). IEEE, pp 625–628
Weitzel L, Prati RC, Aguiar RF (2016) The comprehension of figurative language: what is the influence of irony and sarcasm on NLP techniques? In: Pedrycz W, ChenShyi-Ming (eds) Sentiment Analysis and Ontology Engineering. Springer International Publishing, pp 49–74
Onan A (2017) Sarcasm identification on twitter: a machine learning approach. In: Artificial intelligence trends in intelligent systems, pp 374–383
Bamman D, Smith NA (2015) Contextualized sarcasm detection on twitter. In: Ninth international AAAI conference on web and social media, pp 574–577
Ptácek T, Habernal I, Hong J, Ptácek T, Habernal I, Hong J (2014) Sarcasm detection on Czech and English twitter, pp 213–223
Altrabsheh N, Cocea M, Fallahkhair S (2015) Detecting sarcasm from students’ feedback in twitter. Springer, Cham, pp 551–555
Bharti SK, Pradhan R, Babu KS, Jena SK (2017) Sarcasm analysis on twitter data using machine learning approaches. Springer, Berlin, pp 51–76
Saha S, Yadav J, Ranjan P (2017) Proposed approach for sarcasm detection in twitter. Indian J Sci Technol 8
Zhang M, Zhang Y, Fu G (2016) Tweet sarcasm detection using deep neural network. In: COLING, pp 2449–2460
Jain S, Hsu V (2015) The lowest form of wit: identifying sarcasm in social media
del Pilar Salas-Zárate M, Paredes-Valverde MA, Rodriguez-García MÁ, Valencia-García R, Alor-Hernández G (2017) Automatic detection of satire in twitter: a psycholinguistic-based approach. Knowl Based Syst 128:20–33. https://doi.org/10.1016/j.knosys.2017.04.009
Reganti AN, Maheshwari T, Kumar U, Das A, Bajpai R (2016) Modeling satire in English text for automatic detection. In: IEEE 16th International conference on data mining workshops (ICDMW). IEEE, pp 970–977
Barberi F, Ronzano F, Saggion H (2015) Is this tweet satirical? A computational approach for satire detection in Spanish. Proces del Leng Nat 55:135–142
Zhang R, Liu N (2014) Recognizing humor on twitter. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management—CIKM’14. ACM Press, New York, pp 889–898
Whissell C (2009) Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural language. Psychol Rep 105:509–521. https://doi.org/10.2466/PR0.105.2.509-521
Burgers C, van Mulken M, Schellens PJ (2012) Verbal irony. J Lang Soc Psychol 31:290–310. https://doi.org/10.1177/0261927X12446596
Xu H, Santus E, Laszlo A, Huang C-R (2015) LLT-PolyU: Identifying sentiment intensity in ironic tweets. In: SemEval@ NAACL-HLT, pp 673–678
Cliche M (2017) BB_twtr at SemEval-2017 task 4: twitter sentiment analysis with CNNs and LSTMs
Dario S, Gjorgji S, MadjarovIvica D, Dimitrovski I (2015) UNITN: training deep convolutional neural network for twitter sentiment classification. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Denver, Colorado, pp 464–469
Acknowledgements
Authors María del Pilar Salas-Zárate and Mario Andrés Paredes-Valverde are supported by the National Council of Science and Technology (CONACYT), the Secretariat of Public Education (SEP), and the Mexican government. Additionally, this work was supported by Tecnológico Nacional de Mexico (TecNM) and the Secretariat of Public Education (SEP) through PRODEP (Programa para el Desarrollo Profesional Docente).
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Appendix 1: Keyword-based search queries
Appendix 1: Keyword-based search queries
In this appendix, the search queries that were built to search for primary studies in each one of the selected repositories are presented.
ACM Digital Library: It offers an advanced search to look for keywords in titles and abstracts.
acmdlTitle: (+(“figurative language” irony ironic sarcasm sarcastic satirical satire humor) +(“social web” “social networks” microblogs “social media” twitter Facebook)) OR recordAbstract: (+(“figurative language” irony ironic sarcasm sarcastic satirical satire humor) +(“social web” “social networks” microblogs “social media” twitter Facebook)) Year: 2013–2017
IEEE Xplore Digital Library: The advanced search option allows keywords to be searched in titles and abstracts.
(((“figurative language” OR irony OR ironic OR sarcasm OR sarcastic OR satirical OR satire OR humor) AND (“social networks” OR “social web” OR microblogs OR “social media” OR Twitter OR Facebook))) Year: 2013–2017
ScienceDirect: It provides an advanced search option to look for keywords in abstracts, titles, and in the keywords section of papers.
pub-date > 2012 and TITLE-ABSTR-KEY ((“figurative language” OR irony OR ironic OR sarcasm OR sarcastic OR satirical OR satire OR humor)) and TITLE-ABSTR-KEY ((“social networks” OR “social web” OR microblogs OR “social media” OR Twitter OR Facebook)).
Springer: It provides an advanced search option, but it does not allow keywords to be searched in the abstracts or the titles. Instead, the search must be conducted on the whole documents. For this repository, we reduced the number of searched keywords, since we initially retrieved a great number of irrelevant documents.
irony|sarcasm|”Figurative language” and Twitter|Facebook|“social media”|“social networks” Year: 2013–2017
Wiley Online Library: It offers an advanced search option for keywords to be searched in titles, abstracts, or in whole documents.
(“figurative language” OR irony OR ironic OR sarcasm OR sarcastic OR satirical OR satire OR humor) in Article Titles AND (“social networks” OR “social web” OR microblogs OR “social media” OR Twitter OR Facebook) in Article Titles between years 2013 and 2017
Google Scholar: It provides an advanced search option to search for terms in whole documents or in the titles. We decided to conduct a title search to refine the results.
allintitle: “social web” OR “social networks” OR microblogs OR “social media” OR Facebook OR twitter AND “figurative language” OR irony OR ironic OR sarcasm OR sarcastic OR satirical OR satire OR humor Year: 2013–2017
Web of science: It provides a basic search option where keywords can be searched in the title and other search fields.
Title: ((“figurative language” OR irony OR ironic OR sarcasm OR sarcastic OR satirical OR satire OR humor) AND (“social networks” OR “social web” OR microblogs OR “social media” OR Twitter OR Facebook)) Year: 2013–2017.
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del Pilar Salas-Zárate, M., Alor-Hernández, G., Sánchez-Cervantes, J.L. et al. Review of English literature on figurative language applied to social networks. Knowl Inf Syst 62, 2105–2137 (2020). https://doi.org/10.1007/s10115-019-01425-3
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DOI: https://doi.org/10.1007/s10115-019-01425-3