6533b7d8fe1ef96bd1269b69

RESEARCH PRODUCT

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

Teresa AlcamoGiosuè Lo BoscoDaniele SchicchiGiovanni PilatoAlfredo Cuzzocrea

subject

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 processing

description

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.

yearjournalcountryeditionlanguage
2020-11-30Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services
https://doi.org/10.1145/3428757.3429144