The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification, polarity classification, and irony detection. In 2016 SENTIPOLC has been again the most participated EVALITA task with a total of 57 submitted runs from 13 different teams. We present the datasets – which includes an enriched annotation scheme for dealing with the impact on polarity of a figurative use of language – the evaluation methodology, and discuss results and participating systems.

Overview of the Evalita 2016 Sentiment Polarity Classification Task

Basile, Valerio;PATTI, Viviana
2016-01-01

Abstract

The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification, polarity classification, and irony detection. In 2016 SENTIPOLC has been again the most participated EVALITA task with a total of 57 submitted runs from 13 different teams. We present the datasets – which includes an enriched annotation scheme for dealing with the impact on polarity of a figurative use of language – the evaluation methodology, and discuss results and participating systems.
3rd Italian Conference on Computational Linguistics, CLiC-it 2016 and 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, EVALITA 2016
Napoli
2016
CEUR Workshop Proceedings
CEUR-WS
1749
1
11
http://ceur-ws.org/Vol-1749/paper_026.pdf
Sentiment analysis, Twitter, Irony Detection, Evaluation campaign, Natural Language Processing
Barbieri, Francesco; Basile, Valerio; Croce, Danilo; Nissim, Malvina; Novielli, Nicole; Patti, Viviana
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1644412
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