{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:29:29Z","timestamp":1778106569968,"version":"3.51.4"},"reference-count":104,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T00:00:00Z","timestamp":1592784000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T00:00:00Z","timestamp":1592784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164\u2013175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.<\/jats:p>","DOI":"10.1007\/s00521-020-05102-3","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T03:33:42Z","timestamp":1592796822000},"page":"17309-17320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":205,"title":["A transformer-based approach to irony and sarcasm detection"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0049-2589","authenticated-orcid":false,"given":"Rolandos Alexandros","family":"Potamias","sequence":"first","affiliation":[]},{"given":"Georgios","family":"Siolas","sequence":"additional","affiliation":[]},{"given":"Andreas - Georgios","family":"Stafylopatis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,22]]},"reference":[{"key":"5102_CR1","doi-asserted-by":"crossref","unstructured":"Amir S, Wallace BC, Lyu H, Silva PCMJ (2016) Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976","DOI":"10.18653\/v1\/K16-1017"},{"issue":"10","key":"5102_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0186836","volume":"12","author":"D Antonakaki","year":"2017","unstructured":"Antonakaki D, Spiliotopoulos D, Samaras CV, Pratikakis P, Ioannidis S, Fragopoulou P (2017) Social media analysis during political turbulence. PLoS ONE 12(10):1\u201323","journal-title":"PLoS ONE"},{"key":"5102_CR3","doi-asserted-by":"crossref","unstructured":"Barbieri F, Ronzano F, Saggion H (2015) UPF-taln: SemEval 2015 tasks 10 and 11. Sentiment analysis of literal and figurative language in Twitter. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 704\u2013708","DOI":"10.18653\/v1\/S15-2119"},{"key":"5102_CR4","doi-asserted-by":"crossref","unstructured":"Barbieri F, Saggion H (2014) Modelling irony in Twitter. In: EACL","DOI":"10.3115\/v1\/E14-3007"},{"key":"5102_CR5","doi-asserted-by":"crossref","unstructured":"Baziotis C, Nikolaos A, Papalampidi P, Kolovou A, Paraskevopoulos G, Ellinas N, Potamianos A (2018) NTUA-SLP at SemEval-2018 task 3: tracking ironic tweets using ensembles of word and character level attentive RNNs. In: Proceedings of the 12th international workshop on semantic evaluation. Association for Computational Linguistics, New Orleans, pp 613\u2013621","DOI":"10.18653\/v1\/S18-1100"},{"key":"5102_CR6","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.neuroimage.2013.12.046","volume":"90","author":"M Benedek","year":"2014","unstructured":"Benedek M, Beaty R, Jauk E, Koschutnig K, Fink A, Silvia PJ, Dunst B, Neubauer AC (2014) Creating metaphors: the neural basis of figurative language production. NeuroImage 90:99\u2013106","journal-title":"NeuroImage"},{"key":"5102_CR7","doi-asserted-by":"crossref","unstructured":"Buschmeier K, Cimiano P, Klinger R (2014) An impact analysis of features in a classification approach to irony detection in product reviews. In: Proceedings of the 5th workshop on computational approaches to subjectivity, sentiment and social media analysis. Association for Computational Linguistics, Baltimore, pp 42\u201349","DOI":"10.3115\/v1\/W14-2608"},{"key":"5102_CR8","doi-asserted-by":"crossref","unstructured":"Carvalho P (2009) Clues for detecting irony in user-generated contents: Oh...!! it\u2019s \u201cso easy. In: International CIKM workshop on topic-sentiment analysis for mass opinion measurement, Hong Kong","DOI":"10.1145\/1651461.1651471"},{"key":"5102_CR9","doi-asserted-by":"crossref","unstructured":"Cer D, Yang Y, Kong SY, Hua N, Limtiaco N, John RS, Constant N, Guajardo-Cespedes M, Yuan S, Tar C et\u00a0al (2018) Universal sentence encoder. arXiv preprint arXiv:1803.11175","DOI":"10.18653\/v1\/D18-2029"},{"key":"5102_CR10","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.engappai.2016.01.007","volume":"51","author":"B Charalampakis","year":"2016","unstructured":"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\u201357","journal-title":"Eng Appl Artif Intell"},{"key":"5102_CR11","unstructured":"Chelba C, Mikolov T, Schuster M, Ge Q, Brants T, Koehn P, Robinson T (2013) One billion word benchmark for measuring progress in statistical language modeling. arXiv preprint arXiv:1312.3005"},{"key":"5102_CR12","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1037\/0096-3445.113.1.121","volume":"113","author":"HH Clark","year":"1984","unstructured":"Clark HH, Gerrig RJ (1984) On the pretense theory of irony. J Exp Psychol Gen 113:121\u2013126","journal-title":"J Exp Psychol Gen"},{"issue":"12","key":"5102_CR13","doi-asserted-by":"publisher","first-page":"e115381","DOI":"10.1371\/journal.pone.0115381","volume":"9","author":"V Cuccio","year":"2014","unstructured":"Cuccio V, Ambrosecchia M, Ferri F, Carapezza M, Piparo FL, Fogassi L, Gallese V (2014) How the context matters. Literal and figurative meaning in the embodied language paradigm. PLoS ONE 9(12):e115381","journal-title":"PLoS ONE"},{"key":"5102_CR14","unstructured":"Dai AM, Le QV (2015) Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, pp 3079\u20133087"},{"key":"5102_CR15","doi-asserted-by":"crossref","unstructured":"Dai Z, Yang Z, Yang Y, Cohen WW, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860","DOI":"10.18653\/v1\/P19-1285"},{"key":"5102_CR16","unstructured":"Davidov D, Tsur O, Rappoport A (2010) Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the fourteenth conference on computational natural language learning, CoNLL \u201910. Association for Computational Linguistics, Stroudsburg, pp 107\u2013116"},{"key":"5102_CR17","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: human language technologies, volume 1 (long and short papers). Association for Computational Linguistics, Minneapolis, pp 4171\u20134186"},{"issue":"8","key":"5102_CR18","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1007\/s13042-017-0727-z","volume":"10","author":"A Dridi","year":"2019","unstructured":"Dridi A, Recupero DR (2019) Leveraging semantics for sentiment polarity detection in social media. Int J Mach Learn Cybern 10(8):2045\u20132055","journal-title":"Int J Mach Learn Cybern"},{"key":"5102_CR19","doi-asserted-by":"crossref","unstructured":"Dubey A, Kumar L, Somani A, Joshi A, Bhattacharyya P (2019) \u201cWhen numbers matter!\u201d: detecting sarcasm in numerical portions of text. In: Proceedings of the tenth workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 72\u201380","DOI":"10.18653\/v1\/W19-1309"},{"key":"5102_CR20","doi-asserted-by":"crossref","unstructured":"Far\u00edas DIH, Montes-y-G\u00f3mez M, Escalante HJ, Rosso P, Patti V (2018) A knowledge-based weighted KNN for detecting irony in Twitter. In: Mexican international conference on artificial intelligence. Springer, Berlin, pp 194\u2013206","DOI":"10.1007\/978-3-030-04497-8_16"},{"issue":"3","key":"5102_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1145\/2930663","volume":"16","author":"DIH Far\u00edas","year":"2016","unstructured":"Far\u00edas DIH, Patti V, Rosso P (2016) Irony detection in Twitter: the role of affective content. ACM Trans Internet Technol (TOIT) 16(3):19","journal-title":"ACM Trans Internet Technol (TOIT)"},{"issue":"1","key":"5102_CR22","first-page":"2096\u20132030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096\u20132030","journal-title":"J Mach Learn Res"},{"key":"5102_CR23","doi-asserted-by":"publisher","first-page":"40871","DOI":"10.1038\/srep40871","volume":"7","author":"Z Gao","year":"2017","unstructured":"Gao Z, Gao S, Xu L, Zheng X, Ma X, Luo L, Kendrick KM (2017) Women prefer men who use metaphorical language when paying compliments in a romantic context. Sci Rep 7:40871","journal-title":"Sci Rep"},{"key":"5102_CR24","doi-asserted-by":"crossref","unstructured":"Ghosh A, Li G, Veale T, Rosso P, Shutova E, Barnden J, Reyes A (2015) SemEval-2015 task 11: sentiment analysis of figurative language in Twitter. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 470\u2013478","DOI":"10.18653\/v1\/S15-2080"},{"key":"5102_CR25","doi-asserted-by":"crossref","unstructured":"Ghosh A, Veale T (2016) Fracking sarcasm using neural network. In: Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 161\u2013169","DOI":"10.18653\/v1\/W16-0425"},{"key":"5102_CR26","doi-asserted-by":"crossref","unstructured":"Ghosh D, Guo W, Muresan S (2015) Sarcastic or not: word embeddings to predict the literal or sarcastic meaning of words. In: EMNLP","DOI":"10.18653\/v1\/D15-1116"},{"key":"5102_CR27","doi-asserted-by":"crossref","unstructured":"Gim\u00e9nez M, Pla F, Hurtado LF (2015) ELiRF: a SVM approach for SA tasks in Twitter at SemEval-2015. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 574\u2013581","DOI":"10.18653\/v1\/S15-2096"},{"key":"5102_CR28","unstructured":"Gonz\u00e1ilez-Ib\u00e1\u00f1ez RI, Muresan S, Wacholder N (2011) Identifying sarcasm in Twitter: a closer look. In: ACL"},{"key":"5102_CR29","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge"},{"key":"5102_CR30","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1017\/CBO9780511814273.039","volume-title":"Reasoning: studies of human inference and its foundations","author":"HP Grice","year":"2008","unstructured":"Grice HP (2008) Further notes on logic and conversation. In: Adler JE, Rips LJ (eds) Reasoning: studies of human inference and its foundations. Cambridge University Press, Cambridge, pp 765\u2013773"},{"key":"5102_CR31","unstructured":"Gupta U, Chatterjee A, Srikanth R, Agrawal P (2017) A sentiment-and-semantics-based approach for emotion detection in textual conversations"},{"key":"5102_CR32","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1037\/0096-3445.115.1.3","volume":"115","author":"RW Gibbs","year":"1986","unstructured":"Gibbs RW (1986) On the psycholinguistics of sarcasm. J Exp Psychol Gen 115:3\u201315","journal-title":"J Exp Psychol Gen"},{"issue":"4","key":"5102_CR33","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s10462-016-9489-3","volume":"47","author":"V Hangya","year":"2017","unstructured":"Hangya V, Farkas R (2017) A comparative empirical study on social media sentiment analysis over various genres and languages. Artif Intell Rev 47(4):485\u2013505","journal-title":"Artif Intell Rev"},{"key":"5102_CR34","unstructured":"Hazarika D, Poria S, Gorantla S, Cambria E, Zimmermann R, Mihalcea R (2018) Cascade: contextual sarcasm detection in online discussion forums. arXiv preprint arXiv:1805.06413"},{"key":"5102_CR35","unstructured":"Hee CV, Lefever E, Hoste V (2018) SemEval-2018 task 3: irony detection in English tweets. In: SemEval@NAACL-HLT"},{"key":"5102_CR36","doi-asserted-by":"crossref","unstructured":"Hiai S, Shimada K (2018) Sarcasm detection using features based on indicator and roles. In: International conference on soft computing and data mining. Springer, Berlin, pp 418\u2013428","DOI":"10.1007\/978-3-319-72550-5_40"},{"key":"5102_CR37","doi-asserted-by":"crossref","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. In: Proceedings of the 56th annual meeting of the Association for Computational Linguistics (volume 1: long papers). Association for Computational Linguistics, Melbourne, pp 328\u2013339","DOI":"10.18653\/v1\/P18-1031"},{"key":"5102_CR38","doi-asserted-by":"crossref","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146","DOI":"10.18653\/v1\/P18-1031"},{"key":"5102_CR39","doi-asserted-by":"crossref","unstructured":"Huang YH, Huang HH, Chen HH (2017) Irony detection with attentive recurrent neural networks. In: ECIR","DOI":"10.1007\/978-3-319-56608-5_45"},{"key":"5102_CR40","doi-asserted-by":"crossref","unstructured":"Ili\u0107 S, Marrese-Taylor E, Balazs JA, Matsuo Y (2018) Deep contextualized word representations for detecting sarcasm and irony. arXiv preprint arXiv:1809.09795","DOI":"10.18653\/v1\/W18-6202"},{"key":"5102_CR41","doi-asserted-by":"crossref","unstructured":"Iyyer M, Manjunatha V, Boyd-Graber J, Daum\u00e9\u00a0III H (2015) Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 1: long papers). Association for Computational Linguistics, Beijing, pp 1681\u20131691","DOI":"10.3115\/v1\/P15-1162"},{"key":"5102_CR42","doi-asserted-by":"publisher","first-page":"23253","DOI":"10.1109\/ACCESS.2017.2776930","volume":"6","author":"Z Jianqiang","year":"2018","unstructured":"Jianqiang Z, Xiaolin G, Xuejun Z (2018) Deep convolution neural networks for Twitter sentiment analysis. IEEE Access 6:23253\u201323260","journal-title":"IEEE Access"},{"key":"5102_CR43","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/B978-0-12-812056-9.00016-6","volume-title":"Integrating disaster science and management","author":"JK Joseph","year":"2018","unstructured":"Joseph JK, Dev KA, Pradeepkumar AP, Mohan M (2018) Chapter 16\u2014Big data analytics and social media in disaster management. In: Samui P, Kim D, Ghosh CBTIDS (eds) Integrating disaster science and management. Elsevier, Amsterdam, pp 287\u2013294"},{"key":"5102_CR44","doi-asserted-by":"crossref","unstructured":"Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O (2019) Spanbert: improving pre-training by representing and predicting spans. arXiv preprint arXiv:1907.10529","DOI":"10.1162\/tacl_a_00300"},{"key":"5102_CR45","unstructured":"Joulin A, Grave E, Bojanowski P, Douze M, J\u00e9gou H, Mikolov T (2016) Fasttext. zip: compressing text classification models. arXiv preprint arXiv:1612.03651"},{"key":"5102_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jneuroling.2012.07.001","volume":"26","author":"K Kasparian","year":"2013","unstructured":"Kasparian K (2013) Hemispheric differences in figurative language processing: contributions of neuroimaging methods and challenges in reconciling current empirical findings. J Neuroling 26:1\u201321","journal-title":"J Neuroling"},{"key":"5102_CR47","volume-title":"Propositional structure and illocutionary force: a study of the contribution of sentence meaning to speech acts\/Jerrold J. Katz. The Language and thought series","author":"JJ Katz","year":"1977","unstructured":"Katz JJ (1977) Propositional structure and illocutionary force: a study of the contribution of sentence meaning to speech acts\/Jerrold J. Katz. The Language and thought series. Crowell, New York"},{"key":"5102_CR48","unstructured":"Khodak M, Saunshi N, Vodrahalli K (2017) A large self-annotated corpus for sarcasm. arXiv e-prints"},{"key":"5102_CR49","unstructured":"Kim E, Klinger R (2018) A survey on sentiment and emotion analysis for computational literary studies"},{"key":"5102_CR50","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv e-prints"},{"key":"5102_CR51","doi-asserted-by":"crossref","unstructured":"Kumar A, Garg G (2019) Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets. J Ambient Intell Humaniz Comput 1\u201316","DOI":"10.1007\/s12652-019-01419-7"},{"key":"5102_CR52","doi-asserted-by":"publisher","first-page":"23319","DOI":"10.1109\/ACCESS.2019.2899260","volume":"7","author":"A Kumar","year":"2019","unstructured":"Kumar A, Sangwan SR, Arora A, Nayyar A, Abdel-Basset M et al (2019) Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access 7:23319\u201323328","journal-title":"IEEE Access"},{"key":"5102_CR53","unstructured":"Kumar L, Somani A, Bhattacharyya P (2017) \u201cHaving 2 hours to write a paper is fun!\u201d: detecting sarcasm in numerical portions of text. arXiv e-prints"},{"key":"5102_CR54","doi-asserted-by":"crossref","unstructured":"Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"5102_CR55","unstructured":"Lample G, Conneau A (2019) Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291"},{"issue":"5915","key":"5102_CR56","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1126\/science.1167742","volume":"323","author":"D Lazer","year":"2009","unstructured":"Lazer D, Pentland A, Adamic L, Aral S, Barabasi AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D, Van Alstyne M (2009) Life in the network: the coming age of computational social science. Science (New York, N. Y.) 323(5915):721\u2013723","journal-title":"Science (New York, N. Y.)"},{"key":"5102_CR57","doi-asserted-by":"crossref","unstructured":"Ling J, Klinger R (2016) An empirical, quantitative analysis of the differences between sarcasm and irony. In: European semantic web conference. Springer, Berlin, pp 203\u2013216","DOI":"10.1007\/978-3-319-47602-5_39"},{"key":"5102_CR58","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139084789","volume-title":"Sentiment analysis\u2014mining opinions, sentiments, and emotions","author":"B Liu","year":"2015","unstructured":"Liu B (2015) Sentiment analysis\u2014mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge"},{"key":"5102_CR59","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"5102_CR60","unstructured":"Loenneker-Rodman B, Narayanan S (2010) Computational approaches to figurative language. Cambridge Encyclopedia of Psycholinguistics. Cambridge University Press, Cambridge"},{"key":"5102_CR61","unstructured":"McCann B, Bradbury J, Xiong C, Socher R (2017) Learned in translation: Contextualized word vectors. In: Advances in Neural Information Processing Systems, pp 6294\u20136305"},{"key":"5102_CR62","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv e-prints"},{"key":"5102_CR63","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. arXiv e-prints"},{"key":"5102_CR64","volume-title":"Design and analysis of experiments","author":"DC Montgomery","year":"2017","unstructured":"Montgomery DC (2017) Design and analysis of experiments, 9th edn. Wiley, New York","edition":"9"},{"key":"5102_CR65","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Grishman R (2015) Relation extraction: perspective from convolutional neural networks. In: Proceedings of the 1st workshop on vector space modeling for natural language processing. Association for Computational Linguistics, Denver, pp 39\u201348","DOI":"10.3115\/v1\/W15-1506"},{"key":"5102_CR66","doi-asserted-by":"crossref","unstructured":"Nozza D, Fersini E, Messina E (2016) Unsupervised irony detection: a probabilistic model with word embeddings. In: KDIR, pp 68\u201376","DOI":"10.5220\/0006052000680076"},{"key":"5102_CR67","doi-asserted-by":"crossref","unstructured":"Oboler A, Welsh K, Cruz L (2012) The danger of big data: social media as computational social science. First Monday 17(7)","DOI":"10.5210\/fm.v17i7.3993"},{"key":"5102_CR68","unstructured":"Ortega-Bueno R, Rangel F, Hern\u00e1ndez\u00a0Far\u0131as D, Rosso P, Montes-y-G\u00f3mez M, Medina\u00a0Pagola JE (2019) Overview of the task on irony detection in Spanish variants. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), co-located with 34th conference of the Spanish Society for natural language processing (SEPLN 2019). CEUR-WS.org"},{"key":"5102_CR69","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir C, Bergler S (2015) CLaC-SentiPipe: SemEval2015 subtasks 10 B, E, and task 11. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 479\u2013485","DOI":"10.18653\/v1\/S15-2081"},{"key":"5102_CR70","volume-title":"Linguistic inquiry and word count","author":"J Pennebaker","year":"1999","unstructured":"Pennebaker J, Francis M (1999) Linguistic inquiry and word count. Lawrence Erlbaum Associates, Incorporated, Mahwah"},{"key":"5102_CR71","first-page":"1532","volume":"14","author":"J Pennington","year":"2014","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. EMNLP 14:1532\u20131543","journal-title":"EMNLP"},{"key":"5102_CR72","doi-asserted-by":"crossref","unstructured":"Peters ME, Ammar W, Bhagavatula C, Power R (2017) Semi-supervised sequence tagging with bidirectional language models. arXiv preprint arXiv:1705.00108","DOI":"10.18653\/v1\/P17-1161"},{"key":"5102_CR73","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv preprint arXiv:1802.05365","DOI":"10.18653\/v1\/N18-1202"},{"key":"5102_CR74","doi-asserted-by":"crossref","unstructured":"Potamias RA, Neofytou A, Siolas G (2019) NTUA-ISLab at SemEval-2019 task 9: mining suggestions in the wild. In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, pp 1224\u20131230","DOI":"10.18653\/v1\/S19-2215"},{"key":"5102_CR75","doi-asserted-by":"crossref","unstructured":"Potamias RA, Siolas G (2019) NTUA-ISLab at SemEval-2019 task 3: determining emotions in contextual conversations with deep learning. In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, pp 277\u2013281","DOI":"10.18653\/v1\/S19-2047"},{"key":"5102_CR76","doi-asserted-by":"crossref","unstructured":"Potamias RA, Siolas G, Stafylopatis A (2019) A robust deep ensemble classifier for figurative language detection. In: International conference on engineering applications of neural networks. Springer, Berlin, pp 164\u2013175","DOI":"10.1007\/978-3-030-20257-6_14"},{"key":"5102_CR77","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training"},{"key":"5102_CR78","doi-asserted-by":"crossref","unstructured":"Rajadesingan A, Zafarani R, Liu H (2015) Sarcasm detection on Twitter: a behavioral modeling approach. In: WSDM","DOI":"10.1145\/2684822.2685316"},{"key":"5102_CR79","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.knosys.2016.12.018","volume":"120","author":"K Ravi","year":"2017","unstructured":"Ravi K, Ravi V (2017) A novel automatic satire and irony detection using ensembled feature selection and data mining. Knowl Based Syst 120:15\u201333","journal-title":"Knowl Based Syst"},{"key":"5102_CR80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.datak.2012.02.005","volume":"74","author":"A Reyes","year":"2012","unstructured":"Reyes A, Rosso P, Buscaldi D (2012) From humor recognition to irony detection: the figurative language of social media. Data Knowl Eng 74:1\u201312","journal-title":"Data Knowl Eng"},{"issue":"1","key":"5102_CR81","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s10579-012-9196-x","volume":"47","author":"A Reyes","year":"2013","unstructured":"Reyes A, Rosso P, Veale T (2013) A multidimensional approach for detecting irony in Twitter. Lang Resour Eval 47(1):239\u2013268","journal-title":"Lang Resour Eval"},{"key":"5102_CR82","unstructured":"Riloff E, Qadir A, Surve P, De\u00a0Silva L, Gilbert N, Huang R (2013) Sarcasm as contrast between a positive sentiment and negative situation. In: EMNLP 2013\u20142013 conference on empirical methods in natural language processing, proceedings of the conference. Association for Computational Linguistics (ACL), pp 704\u2013714"},{"key":"5102_CR83","doi-asserted-by":"crossref","unstructured":"Rosenthal S, Ritter A, Nakov P, Stoyanov V (2014) SemEval-2014 task 9: sentiment analysis in Twitter. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014). Association for Computational Linguistics, Dublin, pp 73\u201380","DOI":"10.3115\/v1\/S14-2009"},{"key":"5102_CR84","doi-asserted-by":"crossref","unstructured":"Singh NK, Tomar DS, Sangaiah AK (2020) Sentiment analysis: a review and comparative analysis over social media. J Ambient Intell Human Comput 11:97\u2013117","DOI":"10.1007\/s12652-018-0862-8"},{"key":"5102_CR85","first-page":"295","volume-title":"Radical pragmatics","author":"D Sperber","year":"1981","unstructured":"Sperber D, Wilson D (1981) Irony and the use-mention distinction. In: Cole P (ed) Radical pragmatics. Academic Press, New York, pp 295\u2013318"},{"key":"5102_CR86","unstructured":"Stranisci M, Bosco C, Farias H, Irazu D, Patti V (2016) Annotating sentiment and irony in the online italian political debate on #labuonascuola. In: Tenth international conference on language resources and evaluation LREC 2016. ELRA, pp 2892\u20132899"},{"key":"5102_CR87","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.knosys.2016.05.035","volume":"108","author":"E Sulis","year":"2016","unstructured":"Sulis E, Far\u00edas DIH, Rosso P, Patti V, Ruffo G (2016) Figurative messages and affect in Twitter: differences between #irony, #sarcasm and #not. Knowl Based Syst 108:132\u2013143","journal-title":"Knowl Based Syst"},{"key":"5102_CR88","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104\u20133112"},{"key":"5102_CR89","doi-asserted-by":"crossref","unstructured":"Tay Y, Luu AT, Hui SC, Su J (2018) Reasoning with sarcasm by reading in-between. In: Proceedings of the 56th annual meeting of the Association for Computational Linguistics (volume 1: long papers). Association for Computational Linguistics, Melbourne, pp 1010\u20131020","DOI":"10.18653\/v1\/P18-1093"},{"key":"5102_CR90","doi-asserted-by":"crossref","unstructured":"Van\u00a0Hee C, Lefever E, Hoste V (2015) LT3: sentiment analysis of figurative tweets\u2014piece of cake #notreally. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 684\u2013688","DOI":"10.18653\/v1\/S15-2115"},{"issue":"3","key":"5102_CR91","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s10579-018-9414-2","volume":"52","author":"C Van Hee","year":"2018","unstructured":"Van Hee C, Lefever E, Hoste V (2018) Exploring the fine-grained analysis and automatic detection of irony on Twitter. Lang Resour Eval 52(3):707\u2013731","journal-title":"Lang Resour Eval"},{"key":"5102_CR92","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"5102_CR93","doi-asserted-by":"crossref","unstructured":"Wallace BC, Choe DK, Charniak E (2015) Sparse, contextually informed models for irony detection: exploiting user communities, entities and sentiment. In: ACL-IJCNLP 2015\u201453rd annual meeting of the Association for Computational Linguistics (ACL), proceedings of the conference, vol\u00a01","DOI":"10.3115\/v1\/P15-1100"},{"key":"5102_CR94","unstructured":"Wang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics: short papers, vol 2. Association for Computational Linguistics, pp 90\u201394"},{"key":"5102_CR95","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3389\/fnhum.2014.00583","volume":"8","author":"H Weiland","year":"2014","unstructured":"Weiland H, Bambini V, Schumacher PB (2014) The role of literal meaning in figurative language comprehension: evidence from masked priming ERP. Front Hum Neurosci 8:583","journal-title":"Front Hum Neurosci"},{"key":"5102_CR96","volume-title":"The social fact","author":"JP Winbey","year":"2019","unstructured":"Winbey JP (2019) The social fact. The MIT Press, Cambridge"},{"key":"5102_CR97","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Wu S, Liu J, Yuan Z, Huang Y (2018) THU\\_ngn at SemEval-2018 task 3: tweet irony detection with densely connected LSTM and multi-task learning. In: SemEval@NAACL-HLT","DOI":"10.18653\/v1\/S18-1006"},{"key":"5102_CR98","unstructured":"Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K et\u00a0al (2016) Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144"},{"key":"5102_CR99","doi-asserted-by":"crossref","unstructured":"Xu H, Santus E, Laszlo A, Huang CR (2015) LLT-PolyU: identifying sentiment intensity in ironic tweets. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 673\u2013678","DOI":"10.18653\/v1\/S15-2113"},{"key":"5102_CR100","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV (2019) Xlnet: generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237"},{"key":"5102_CR101","unstructured":"You Y, Li J, Hseu J, Song X, Demmel J, Hsieh CJ (2019) Reducing bert pre-training time from 3 days to 76 min. arXiv preprint arXiv:1904.00962"},{"issue":"5","key":"5102_CR102","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1016\/j.ipm.2019.04.006","volume":"56","author":"S Zhang","year":"2019","unstructured":"Zhang S, Zhang X, Chan J, Rosso P (2019) Irony detection via sentiment-based transfer learning. Inf Process Manag 56(5):1633\u20131644","journal-title":"Inf Process Manag"},{"key":"5102_CR103","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","volume":"237","author":"L Zhou","year":"2017","unstructured":"Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: opportunities and challenges. Neurocomputing 237:350\u2013361","journal-title":"Neurocomputing"},{"key":"5102_CR104","doi-asserted-by":"crossref","unstructured":"Zhu Y, Kiros R, Zemel R, Salakhutdinov R, Urtasun R, Torralba A, Fidler S (2015) Aligning books and movies: towards story-like visual explanations by watching movies and reading books. In: Proceedings of the IEEE international conference on computer vision, pp 19\u201327","DOI":"10.1109\/ICCV.2015.11"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05102-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05102-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05102-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T18:09:21Z","timestamp":1667066961000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05102-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,22]]},"references-count":104,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["5102"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05102-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,22]]},"assertion":[{"value":"12 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}