6533b86ffe1ef96bd12cd3f4
RESEARCH PRODUCT
Overview of the Evalita 2014 SENTIment POLarity Classification Task
Valerio BasileViviana PattiMalvina NissimAndrea BolioliPaolo Rossosubject
Polarity (physics)Computer science02 engineering and technologycomputer.software_genreNLP[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Task (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]020204 information systems0202 electrical engineering electronic engineering information engineeringSentiment Analysis[SHS.LANGUE]Humanities and Social Sciences/LinguisticsEvaluationsentiment analysis; twitter; irony; NLPironybusiness.industrySentiment analysis[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-TT]Computer Science [cs]/Document and Text Processingtwitter020201 artificial intelligence & image processingArtificial intelligencebusinessIrony detectionSocial MediacomputerNatural language processingdescription
International audience; English. The SENTIment POLarity Classification Task (SENTIPOLC), a new shared task in the Evalita evaluation campaign , focused on sentiment classification at the message level on Italian tweets. It included three subtasks: subjectivity classification, polarity classification, and irony detection. SENTIPOLC was the most participated Evalita task with a total of 35 submitted runs from 11 different teams. We present the datasets and the evaluation methodology, and discuss results and participating systems. Italiano. Descriviamo modalit a e risultati della campagna di valutazione di sistemi di sentiment analysis (SENTIment POLarity Classification Task), proposta per la prima volta a " Evalita–2014: Evaluation of NLP and Speech Tools for Ital-ian ". In SENTIPOLC e stata valutata la capacit a dei sistemi di riconoscere il sentiment espresso nei messaggi Twitter in lingua italiana. Sono stati proposti tre sotto-task: subjectivity classification, polarity classification e un sotto-task pilota di irony detection. La campagna ha susci-tato molto interesse e ricevuto un totale di 35 run inviati da 11 gruppi di partecipanti.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-12-11 |