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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 3 4 5 6 7 8 Hits 41 – 60 of 148 41 On the Use of Character n-grams as the only Intrinsic Evidence of Plagiarism Rosso, Paolo; Bensalem, Imene; Chikhi, Salim. - : Springer-Verlag, 2019 BASE Show details 42 Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter Ghanem, Bilal; Montes-y-Gómez, Manuel; Frenda, Simona. - : IOS Press, 2019 BASE Show details 43 On the use of word embedding for cross language plagiarism detection Asghari, Habibollah; Fatemi, Omid; Mohtaj, Salar. - : IOS Press, 2019 BASE Show details 44 Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection Daelemans, Walter; Kestemont, Mike; Manjavacas, Enrique. - : Springer-Verlag, 2019 BASE Show details 45 On Language and Structure in Polarized Communities Lai, Mirko. - : Universitat Politècnica de València, 2019 BASE Show details 46 Improving Attitude Words Classification for Opinion Mining using Word Embedding Ortega-Bueno, Reynier; Medina-Pagola, José E.; Muñiz-Cuza, Carlos Enrique. - : Springer-Verlag, 2019 BASE Show details 47 Classifier combination approach for question classification for Bengali question answering system Banerjee, Somnath; Bndyopadhyay, Sivaji; Rosso, Paolo. - : Springer-Verlag, 2019 BASE Show details 48 A Decade of Shared Tasks in Digital Text Forensics at PAN Potthast, Martin; Rosso, Paolo; Stamatatos, Efstathios. - : Springer-Verlag, 2019 BASE Show details 49 Paraphrase Plagiarism Identifcation with Character-level Features Stamatatos, Efstathios; Rosso, Paolo; Sánchez-Vega, Fernando. - : Springer-Verlag, 2019 BASE Show details 50 UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering ... Franco-Salvador, Marc; Kar, Sudipta; Solorio, Thamar. - : arXiv, 2018 BASE Show details 51 A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ... Glavaš, Goran; Franco-Salvador, Marc; Ponzetto, Simone Paolo. - : arXiv, 2018 BASE Show details 52 A Low Dimensionality Representation for Language Variety Identification Rangel-Pardo, Francisco Manuel; Franco-Salvador, Marc; Rosso, Paolo. - : Springer-Verlag, 2018 BASE Show details 53 Overview of PAN 2018. Author identification, author profiling, and author obfuscation Potthast, Martin; Tschuggnall, Michael; Stein, Benno. - : Springer-Verlag, 2018 BASE Show details 54 A resource-light method for cross-lingual semantic textual similarity Glavas, Goran; Rosso, Paolo; Franco-Salvador, Marc. - : Elsevier, 2018 BASE Show details 55 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 56 Character N-Grams for Detecting Deceptive Controversial Opinions Rosso, Paolo; Montes Gomez, Manuel; Luis Villaseñor Pineda. - : Springer-Verlag, 2018 BASE Show details 57 Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach Banerjee, Somnath; Kumar Naskar, Sudip; Rosso, Paolo. - : IOS Press, 2018 BASE Show details 58 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 59 Semantically-informed distance and similarity measures for paraphrase plagiarism identification Álvarez Carmona, M.A.; Franco-Salvador, Marc; Villatoro-Tello, Esaú. - : IOS Press, 2018 BASE Show details 60 A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter Gopal Patra, Braja; Mazumda, Soumadeep; Das, Dipankar; Rosso, Paolo; Bandyopadhyay, Sivaji. - : Springer-Verlag, 2018 Abstract: [EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170. ; The work reported in this paper is supported by a grant from the project “CLIA System Phase II” funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030). ; Gopal Patra, B.; Mazumda, S.; Das, D.; Rosso, P.; Bandyopadhyay, S. (2018). A Multilevel Approach to Sentiment Analysis of Figurative Language in Twitter. Lecture Notes in Computer Science. 9624:281-291. https://doi.org/10.1007/978-3-319-75487-1_22 ; S ; 281 ; 291 ; 9624 ; Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J.: Semeval-2015 task 11: sentiment analysis of figurative language in Twitter. In: 9th International Workshop on Semantic Evaluation (SemEval), Co-located with NAACL, Denver, Colorado, pp. 470–478. Association for Computational Linguistics (2015) ; Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in Twitter. Lang. Resour. Eval. 47(1), 239–268 (2013) ; Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012) ; Patra, B.G., Mandal, S., Das, D., Bandyopadhyay, S.: JU_CSE: a conditional random field (CRF) based approach to aspect based sentiment analysis. In: 8th International Workshop on Semantic Evaluation (SemEval), Co-located with COLING, Dublin, Ireland, pp. 370–374. Association for Computational Linguistics (2014) ; Ozdemir, C., Bergler, S.: CLaC-SentiPipe: SemEval2015 subtasks 10 B, E, and task 11. In: 9th International Workshop on Semantic Evaluation (SemEval), Co-located with NAACL, Denver, Colorado, pp. 479–485. Association for Computational Linguistics (2015) ; Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of wordnet. In: 4th International Conference on Language Resources and Evaluation, pp. 1083–1086 (2004) ; Léger, J.C.: Menger curvature and rectifiability. Ann. Math. 149, 831–869 (1999) ; Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: 18th International Conference on Machine Learning, pp. 282–289 (2001) ; de Albornoz, J.C., Plaza, L., Gervas, P.: SentiSense: an easily scalable concept-based affective lexicon for sentiment analysis. In: 8th International Conference on Language Resources and Evaluation, pp. 3562–3567 (2012) ; Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011) ; Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast: a content-based analysis of interestingness on Twitter. In: 3rd International Web Science Conference. ACM (2011) ; Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., Smith, N.A.: Improved part-of-speech tagging for online conversational text with word clusters. In: NAACL. Association for Computational Linguistics (2013) ; Mohammad, S., Turney, P.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013) ; Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: 7th Conference on International Language Resources and Evaluation, Valletta, Malta (2010) ; Choi, Y., Wiebe, J.: +/ $$-$$ EffectWordNet: sense-level lexicon acquisition for opinion inference. In: EMNLP (2014) ; Whissell, C., Fournier, M., Pelland, R., Weir, D., Makarec, K.: A dictionary of affect in language: IV. Reliability, validity, and applications. Percept. Mot. Skills 62(3), 875–888 (1986) ; Patra, B.G., Takamura, H., Das, D., Okumura, M., Bandyopadhyay, S.: Construction of emotional lexicon using potts model. In: International Joint Conference on Natural Language Processing (IJCNLP), pp. 674–679 (2013) ; Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008) ; Vilares, D., Alonso, M.A., Gomez, C.: On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages. J. Assoc. Inf. Sci. Technol. 66(9), 1799–1816 (2015) ; Barbieri, F., Ronzano, F., Saggion, H.: UPF-taln: SemEval 2015 tasks 10 and 11 sentiment analysis of literal and figurative language in Twitter. In: SemEval-2015, pp. 704–708 (2015) Keyword: Figurative text; Irony; LENGUAJES Y SISTEMAS INFORMATICOS; Metaphor; Sarcasm; Sentiment abruptness measure; Sentiment analysis URL: https://doi.org/10.1007/978-3-319-75487-1_22 http://hdl.handle.net/10251/120701 BASE Hide details Page: 1 2 3 4 5 6 7 8

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