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Automatic detection of irony : opinion mining in microblogs and social media / Jihen Karoui, Farah Benamara, Véronique Moriceau.

By: Karoui, Jihen [author.].
Contributor(s): Benamara, Farah [author.] | Moriceau, Véronique [author.].
Material type: materialTypeLabelBookSeries: Cognitive science series: Publisher: London, UK : Hoboken, NJ : ISTE, Ltd. ; Wiley, 2019Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781119671183; 1119671183; 9781119671220; 1119671221; 9781119671152; 1119671159.Subject(s): Natural language processing (Computer science) | Data mining | TECHNOLOGY & ENGINEERING -- Electronics -- General | Data mining | Natural language processing (Computer science)Genre/Form: Electronic books.DDC classification: 006.3/5 Online resources: Wiley Online Library
Contents:
From Opinion Analysis to Figurative Language Treatment -- Toward Automatic Detection of Figurative Language -- A Multilevel Scheme for Irony Annotation in Social Network Content -- Three Models for Automatic Irony Detection -- Towards a Multilingual System for Automatic Irony Detection -- Conclusion -- Categories of Irony Studied in Linguistic Literature.
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).
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From Opinion Analysis to Figurative Language Treatment -- Toward Automatic Detection of Figurative Language -- A Multilevel Scheme for Irony Annotation in Social Network Content -- Three Models for Automatic Irony Detection -- Towards a Multilingual System for Automatic Irony Detection -- Conclusion -- Categories of Irony Studied in Linguistic Literature.

Includes bibliographical references and index.

Online resource; title from PDF title page (John Wiley, viewed October 17, 2019).

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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