| Description |
1 online resource (210 pages) |
|
text file |
|
135.00 |
| 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). |
| Reproduction |
Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2019. |
| System Details |
Mode of access: World Wide Web. |
| Genre |
Electronic books.
|
| Added Author |
Benamara, Farah, author.
|
|
Moriceau, Veronique, author.
|
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Safari, an O'Reilly Media Company.
|
| Standard No. |
9781786303998 |
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