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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 29 1 Dependency Syntax in the Automatic Detection of Irony and Stance ; Sintaxis de dependencias en la detección automática de ironía y posicionamiento Cignarella, Alessandra Teresa. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2022 BASE Show details 2 Enjeux liés à la détection de l’ironie Laperle, Samuel In: Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 : 23e REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL) ; Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03265905 ; Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.55-66 (2021) BASE Show details 3 Dependency Syntax in the Automatic Detection of Irony and Stance Cignarella, Alessandra Teresa. - : Universitat Politècnica de València, 2021 BASE Show details 4 Contributions to the Computational Treatment of Non-literal Language Rohanian, Omid. - : University of Wolverhampton, 2020 BASE Show details 5 Irony Detection in Twitter with Imbalanced Class Distributions Herrera, Francisco; Rosso, Paolo; Hernandez-Farias, Delia Irazu; Prati, Ronaldo. - : IOS Press, 2020 Abstract: [EN] Irony detection is a not trivial problem and can help to improve natural language processing tasks as sentiment analysis. When dealing with social media data in real scenarios, an important issue to address is data skew, i.e. the imbalance between available ironic and non-ironic samples available. In this work, the main objective is to address irony detection in Twitter considering various degrees of imbalanced distribution between classes. We rely on the emotIDM irony detection model. We evaluated it against both benchmark corpora and skewed Twitter datasets collected to simulate a realistic distribution of ironic tweets. We carry out a set of classification experiments aimed to determine the impact of class imbalance on detecting irony, and we evaluate the performance of irony detection when different scenarios are considered. We experiment with a set of classifiers applying class imbalance techniques to compensate class distribution. Our results indicate that by using such techniques, it is possible to improve the performance of irony detection in imbalanced class scenarios. ; The first author was funded by CONACYT project FC-2016/2410. Ronaldo Prati was supported by the São Paulo State (Brazil) research council FAPESP under project 2015/20606-6. Francisco Herrera was partially supported by the Spanish National Research Project TIN2017-89517-P. The work of Paolo Rosso was partially supported by the Spanish MICINN under the research project MISMIS (PGC2018-096212- B-C31) and by the Generalitat Valenciana under the grant PROMETEO/2019/121. ; Hernandez-Farias, DI.; Prati, R.; Herrera, F.; Rosso, P. (2020). Irony Detection in Twitter with Imbalanced Class Distributions. Journal of Intelligent & Fuzzy Systems. 39(2):2147-2163. https://doi.org/10.3233/JIFS-179880 ; S ; 2147 ; 2163 ; 39 ; 2 ; Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20-29. doi:10.1145/1007730.1007735 ; Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953 ; Fernández A. , García S. , Galar M. , Prati R.C. , Krawczyk B. and Herrera F. , Learning from imbalanced data sets, Springer, (2018). ; Haibo He, & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284. doi:10.1109/tkde.2008.239 ; Farías, D. I. H., Patti, V., & Rosso, P. (2016). Irony Detection in Twitter. ACM Transactions on Internet Technology, 16(3), 1-24. doi:10.1145/2930663 ; Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study1. Intelligent Data Analysis, 6(5), 429-449. doi:10.3233/ida-2002-6504 ; Kumon-Nakamura, S., Glucksberg, S., & Brown, M. (1995). How about another piece of pie: The allusional pretense theory of discourse irony. Journal of Experimental Psychology: General, 124(1), 3-21. doi:10.1037/0096-3445.124.1.3 ; López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113-141. doi:10.1016/j.ins.2013.07.007 ; Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.x ; Mohammad, S. M., Zhu, X., Kiritchenko, S., & Martin, J. (2015). Sentiment, emotion, purpose, and style in electoral tweets. Information Processing & Management, 51(4), 480-499. doi:10.1016/j.ipm.2014.09.003 ; Poria, S., Gelbukh, A., Hussain, A., Howard, N., Das, D., & Bandyopadhyay, S. (2013). Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining. IEEE Intelligent Systems, 28(2), 31-38. doi:10.1109/mis.2013.4 ; Prati, R. C., Batista, G. E. A. P. A., & Silva, D. F. (2014). Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowledge and Information Systems, 45(1), 247-270. doi:10.1007/s10115-014-0794-3 ; Reyes, A., Rosso, P., & Veale, T. (2012). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation, 47(1), 239-268. doi:10.1007/s10579-012-9196-x ; 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. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035 ; Utsumi, A. (2000). Verbal irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony. Journal of Pragmatics, 32(12), 1777-1806. doi:10.1016/s0378-2166(99)00116-2 ; Whissell, C. (2009). Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language. Psychological Reports, 105(2), 509-521. doi:10.2466/pr0.105.2.509-521 ; Wilson, D., & Sperber, D. (1992). On verbal irony. Lingua, 87(1-2), 53-76. doi:10.1016/0024-3841(92)90025-e Keyword: Class imbalance; Imbalanced learning; Irony detection; LENGUAJES Y SISTEMAS INFORMATICOS URL: https://doi.org/10.3233/JIFS-179880 http://hdl.handle.net/10251/171314 BASE Hide details 6 Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter Hurtado Oliver, Lluis Felip; González-Barba, José Ángel; Pla Santamaría, Ferran. - : Elsevier, 2020 BASE Show details 7 RGCL at IDAT: deep learning models for irony detection in Arabic language Mandhari, Salim; Mohamed, Emad; Saadany, Hadeel... In: 2517 ; 416 ; 425 (2019) BASE Show details 8 Computational models for irony detection in three Spanish variants Frenda S.; Patti V.. - : CEUR-WS, 2019. : country:ESP, 2019. : place:Bilbao, 2019 BASE Show details 9 Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection Di Gangi, M.; Lo Bosco, G.; Pilato, G.. - 2019 BASE Show details 10 IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets Ghanem, Bilal; Karoui, Jihen; Benamara, Farah. - : CEUR-WS.org, 2019 BASE Show details 11 WLV at SemEval-2018 task 3: Dissecting tweets in search of irony Rohanian, Omid; Taslimipoor, Shiva; Evans, Richard. - : Association for Computational Linguistics, 2018 BASE Show details 12 A survey on author profiling, deception, and irony detection for the Arabic language Rosso P.; Rangel F.; Hernandez Farias Delia Irazu. - 2018 BASE Show details 13 Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges Basile, Valerio; Novielli, Nicole; Croce, Danilo. - 2018 BASE Show details 14 A Knowledge-Based Weighted KNN for Detecting Irony in Twitter Hernandez-Farias, Delia Irazu; Montes Gomez, Manuel; Escalante, Hugo. - : Springer-Verlag, 2018 BASE Show details 15 A survey on author profiling, deception, and irony detection for the Arabic language Zaghouani, Wajdi; Hernandez-Farias, Delia Irazu; Rosso, Paolo. - : Wiley, 2018 BASE Show details 16 SOUKHRIA: Towards an Irony Detection System for Arabic in Social Media Karoui, Jihen; Benamara, Farah; Moriceau, Veronique In: 3rd International Conference on Arabic Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01686504 ; 3rd International Conference on Arabic Computational Linguistics, Nov 2017, Dubaï, United Arab Emirates. pp.161 - 168, ⟨10.1016/j.procs.2017.10.105⟩ (2017) BASE Show details 17 Ironic gestures and tones in twitter Frenda, Simona. - : CEUR-WS, 2017. : country:DEU, 2017. : place:Aachen, 2017 BASE Show details 18 Irony and Sarcasm Detection in Twitter: The Role of Affective Content Hernández Farias, Delia Irazu. - : Universitat Politècnica de València, 2017 BASE Show details 19 Special Session on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis (SentISData 2016) Benamara, Farah; Bosco, Cristina; Fersini, Elisabetta. - : HAL CCSD, 2016 In: 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016) ; https://hal.archives-ouvertes.fr/hal-03176429 ; Benamara, Farah; Bosco, Cristina; Fersini, Elisabetta; Patti, Viviana; Viviancos, Emilio. 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), Oct 2016, Montréal, Canada. 2016 ; https://sites.ualberta.ca/~dsaa16/specialsessions.html (2016) BASE Show details 20 Character and Word Baselines for Irony Detection in Spanish Short Texts ; Sistemas de detección de ironía basados en palabras y caracteres para textos cortos en español Jasso, Gabriela; Meza Ruiz, Iván Vladimir. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2016 BASE Show details Page: 1 2

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