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).