| Description |
1 online resource (210 pages) |
| Summary |
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) |
| Notes |
Copyright © 2019 by John Wiley & Sons 2019 |
| Issuing Body |
Made available through: Safari, an O'Reilly Media Company |
| Notes |
Online resource; Title from title page (viewed November 5, 2019) |
| Form |
Electronic book
|
| Author |
Benamara, Farah, author
|
|
Moriceau, Veronique, author
|
|
O'Reilly for Higher Education (Firm), distributor.
|
|
Safari, an O'Reilly Media Company
|
| ISBN |
178630399X |
|
9781786303998 |
|
