Automatic detection of irony opinion mining in microblogs and social media
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...
| Otros Autores: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, UK : Hoboken, NJ :
ISTE, Ltd. ; Wiley
2019.
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| Colección: | Wiley ebooks.
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| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b40642781*spi |
| Sumario: | 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). |
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| Descripción Física: | 1 recurso electrónico |
| Formato: | Forma de acceso: World Wide Web. |
| Bibliografía: | Incluye referencias bibliográficas e índice. |
| ISBN: | 9781119671183 9781119671220 9781119671152 |
