LEADER 02358nam 2200457 450 001 9910555284903321 005 20230822201551.0 010 $a1-119-67122-1 010 $a1-119-67115-9 010 $a1-119-67118-3 035 $a(CKB)4100000009526199 035 $a(MiAaPQ)EBC5946039 035 $a(CaSebORM)9781786303998 035 $a(EXLCZ)994100000009526199 100 $a20191121d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomatic detection of irony $eopinion mining in microblogs and social media /$fJihen Karoui, Farah Benamara Zitoune, Ve?ronique Moriceau 205 $aFirst edition 210 1$aLondon ;$aHoboken, New Jersey :$cIste :$cWiley,$d[2019] 210 4$dİ2019 215 $a1 online resource (215 pages) 225 0 $aTHEi Wiley ebooks. 311 $a1-78630-399-X 330 $aIn 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). 606 $aNatural language processing (Computer science) 615 0$aNatural language processing (Computer science) 676 $a006.35 700 $aKaroui$b Jihen$01218480 702 $aMoriceau$b Ve?ronique 702 $aBenamara$b Farah 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910555284903321 996 $aAutomatic detection of irony$92817823 997 $aUNINA