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Automatic Detection of Irony /
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...
| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiley-ISTE,
2019.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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|---|---|---|---|
| 001 | OR_on1235775019 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 210201s2019 xx go 000 0 eng d | ||
| 040 | |a TOH |b eng |c TOH |d OCLCQ | ||
| 020 | |a 178630399X | ||
| 020 | |a 9781786303998 | ||
| 029 | 1 | |a AU@ |b 000068846325 | |
| 035 | |a (OCoLC)1235775019 | ||
| 082 | 0 | 4 | |a 006.35 |q OCoLC |2 23/eng/20230216 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Karoui, Jihen, |e author. | |
| 245 | 1 | 0 | |a Automatic Detection of Irony / |c Karoui, Jihen. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |b Wiley-ISTE, |c 2019. | |
| 300 | |a 1 online resource (210 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 365 | |b 135.00 | ||
| 520 | |a 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). | ||
| 542 | |f Copyright © 2019 by John Wiley & Sons |g 2019 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | 0 | |a Online resource; Title from title page (viewed November 5, 2019). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 700 | 1 | |a Benamara, Farah, |e author. | |
| 700 | 1 | |a Moriceau, Veronique, |e author. | |
| 710 | 2 | |a O'Reilly for Higher Education (Firm), |e distributor. | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781786303998/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||