02358nam 2200457 450 991055528490332120230822201551.01-119-67122-11-119-67115-91-119-67118-3(CKB)4100000009526199(MiAaPQ)EBC5946039(CaSebORM)9781786303998(EXLCZ)99410000000952619920191121d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAutomatic detection of irony opinion mining in microblogs and social media /Jihen Karoui, Farah Benamara Zitoune, Véronique MoriceauFirst editionLondon ;Hoboken, New Jersey :Iste :Wiley,[2019]©20191 online resource (215 pages)THEi Wiley ebooks.1-78630-399-X In recent years, there has been a proliferation of opinion-heavy texts on the Web: opinions of Internet users, comments on social networks, etc. Automating the synthesis of opinions has become crucial to gaining an overview on a given topic. Current automatic systems perform well on classifying the subjective or objective character of a document. However, classifications obtained from polarity analysis remain inconclusive, due to the algorithms' inability to understand the subtleties of human language. Automatic Detection of Irony presents, in three stages, a supervised learning approach to predicting whether a tweet is ironic or not. The book begins by analyzing some everyday examples of irony and presenting a reference corpus. It then develops an automatic irony detection model for French tweets that exploits semantic traits and extralinguistic context. Finally, it presents a study of portability in a multilingual framework (Italian, English, Arabic).Natural language processing (Computer science)Natural language processing (Computer science)006.35Karoui Jihen1218480Moriceau VéroniqueBenamara FarahMiAaPQMiAaPQMiAaPQBOOK9910555284903321Automatic detection of irony2817823UNINA