Description

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).

Automatic Detection of Irony: Opinion Mining in Microblogs and Social Media

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£138.95

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Hardback by Jihen Karoui , Farah Benamara

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In recent years, there has been a proliferation of opinion-heavy texts on the Web: opinions of Internet users, comments on... Read more

    Publisher: ISTE Ltd and John Wiley & Sons Inc
    Publication Date: 18/10/2019
    ISBN13: 9781786303998, 978-1786303998
    ISBN10: 178630399X

    Number of Pages: 210

    Non Fiction , Technology, Engineering & Agriculture , Education

    Description

    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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