- -

Applying basic features from sentiment analysis on automatic irony detection

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Applying basic features from sentiment analysis on automatic irony detection

Show full item record

Hernández Farías, I.; Benedí Ruiz, JM.; Rosso, P. (2015). Applying basic features from sentiment analysis on automatic irony detection. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 337-344. https://doi.org/10.1007/978-3-319-19390-8_38

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/64255

Files in this item

Thumbnail
Name: HernandezFarias-B ...
Size: 308.2Kb
Format: PDF
Description: Versión del Autor.
Open/Preview
[Cerrado]
Name: Hernández Farias- ...
Size: 333.2Kb
Format: PDF
Description: Versión editorial
Request a copy of the document

Item Metadata

Title: Applying basic features from sentiment analysis on automatic irony detection
Author: Hernández Farías, Irazú Benedí Ruiz, José Miguel Rosso, Paolo
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from ...[+]
Subjects: Automatic irony detection , Figurative language processing , Sentiment analysis
Copyrigths: Reserva de todos los derechos
ISBN: 978-3-319-19389-2
Source:
Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. (issn: 0302-9743 )
DOI: 10.1007/978-3-319-19390-8_38
Publisher:
Springer International Publishing
Publisher version: http://link.springer.com/chapter/10.1007/978-3-319-19390-8_38
Series: Lecture Notes in Computer Science;9117
Project ID:
info:eu-repo/grantAgreement/CONACyT//218109%2F313683/
info:eu-repo/grantAgreement/EC/FP7/269180/EU/Web Information Quality Evaluation Initiative/
info:eu-repo/grantAgreement/CONACyT//CVU-369616/
info:eu-repo/grantAgreement/MINECO//TIN2012-38603-C02-01/ES/DIANA-APPLICATIONS: FINDING HIDDEN KNOWLEDGE IN TEXTS: APPLICATIONS/
Description: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_38
Thanks:
The National Council for Science and Technology (CONACyT Mexico) has funded the research work of the first author (Grant No.218109/313683, CVU-369616). The research work of third author was carried out inthe framework of ...[+]
Type: Capítulo de libro

References

Alba-Juez, L.: Irony and the other off record strategies within politeness theory. J. Engl. Am. Stud. 16, 13–24 (1995)

Attardo, S.: Irony markers and functions: towards a goal-oriented theory of irony and its processing. Rask 12, 3–20 (2000)

Barbieri, F., Saggion, H.: Modelling Irony in Twitter, pp. 56–64. Association for Computational Linguistics (2014) [+]
Alba-Juez, L.: Irony and the other off record strategies within politeness theory. J. Engl. Am. Stud. 16, 13–24 (1995)

Attardo, S.: Irony markers and functions: towards a goal-oriented theory of irony and its processing. Rask 12, 3–20 (2000)

Barbieri, F., Saggion, H.: Modelling Irony in Twitter, pp. 56–64. Association for Computational Linguistics (2014)

Bosco, C., Patti, V., Bolioli, A.: Developing corpora for sentiment analysis: the case of irony and senti-tut. IEEE Intell. Syst. 28(2), 55–63 (2013)

Buschmeier, K., Cimiano, P., Klinger, R.: 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, pp. 42–49. Association for Computational Linguistics (2014)

Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J.: Sentiment analysis of figurative language in twitter. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2015), Co-located with NAACL and *SEM (2015)

Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177(2004)

Maynard, D., Greenwood, M.: Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014), European Language Resources Association (ELRA) (2014)

Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet::similarity: measuring the relatedness of concepts. In: Proceedings of the 9th National Conference on Artificial Intelligence, pp. 1024–1025. Association for Computational Linguistics

Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)

Wallace, B.C.: Computational irony: a survey and new perspectives. Artif. Intell. Rev. 43, 467–483 (2013)

Wang, A.P.: #irony or #sarcasm – a quantitative and qualitative study based on twitter. In: Proceedings of the PACLIC: the 27th Pacific Asia Conference on Language, Information, and Computation, pp. 349–356. Department of English, National Chengchi University (2013)

Whissell, C.: Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural languages. Psychol. Rep. 2, 509–521 (2009)

Wolf, A.: Emotional expression online: gender differences in emoticon use. CyberPsychology Behavior 3, 827–833 (2000)

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record