Search results for "Irony detection"

showing 3 items of 3 documents

Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

2020

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.

Text corpusComputer sciencemedia_common.quotation_subjectCompromiseFace (sociological concept)02 engineering and technologycomputer.software_genreField (computer science)020204 information systems0202 electrical engineering electronic engineering information engineeringnatural language processingmedia_commonSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaSarcasmbusiness.industryDeep learningSentiment analysisdeep learningirony detectionIrony020201 artificial intelligence & image processingArtificial intelligencebusinesscomputersarcasm detectionNatural language processingProceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services
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Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection

2019

Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine learning algorithms to input te…

FOS: Computer and information sciencesLinguistics and LanguageComputer Science - Machine LearningComputer sciencemedia_common.quotation_subjectSemantic spaceMachine Learning (stat.ML)02 engineering and technologycomputer.software_genreLanguage and LinguisticsTask (project management)Data-drivenMachine Learning (cs.LG)Artificial IntelligenceStatistics - Machine Learning020204 information systemsEveryday language0202 electrical engineering electronic engineering information engineeringSocial medianatural language processingmedia_commonComputer Science - Computation and LanguageSarcasmSettore INF/01 - Informaticabusiness.industryirony detectionIronymachine learningsemantic spaces020201 artificial intelligence & image processingArtificial intelligencebusinessIrony detectionsemantic spacecomputerComputation and Language (cs.CL)SoftwareNatural language processingsarcasm detection
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Overview of the Evalita 2014 SENTIment POLarity Classification Task

2014

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 …

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 processing
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