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Automatic Detection of Irony

Author Jihen Karoui , Farah Benamara , Veronique Moriceau
Release Date: 2019/11/01
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Book 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).

Table of Contents

  1. Cover
  2. Preface
  3. Introduction
    1. I.1. Context and purpose
    2. I.2. Figurative language: the basics
    3. I.3. Contributions
  4. 1 From Opinion Analysis to Figurative Language Treatment
    1. 1.1. Introduction
    2. 1.2. Defining the notion of opinion
    3. 1.3. Limitations of opinion analysis systems
    4. 1.4. Definition of figurative language
    5. 1.5. Figurative language: a challenge for NLP
    6. 1.6. Conclusion
  5. 2 Toward Automatic Detection of Figurative Language
    1. 2.1. Introduction
    2. 2.2. The main corpora used for figurative language
    3. 2.3. Automatic detection of irony, sarcasm and satire
    4. 2.4. Automatic detection of metaphor
    5. 2.5. Automatic detection of comparison
    6. 2.6. Automatic detection of humor
    7. 2.7. Conclusion
  6. 3 A Multilevel Scheme for Irony Annotation in Social Network Content
    1. 3.1. Introduction
    2. 3.2. The FrIC
    3. 3.3. Multilevel annotation scheme
    4. 3.4. The annotation campaign
    5. 3.5. Results of the annotation campaign
    6. 3.6. Conclusion
  7. 4 Three Models for Automatic Irony Detection
    1. 4.1. Introduction
    2. 4.2. The FrICAuto corpus
    3. 4.3. The SurfSystem model: irony detection based on surface features
    4. 4.4. The PragSystem model: irony detection based on internal contextual features
    5. 4.5. The QuerySystem model: developing a pragmatic contextual approach for automatic irony detection
    6. 4.6. Conclusion
  8. 5 Towards a Multilingual System for Automatic Irony Detection
    1. 5.1. Introduction
    2. 5.2. Irony in Indo-European languages
    3. 5.3. Irony in Semitic languages
    4. 5.4. Conclusion
  9. Conclusion
  10. Appendix: Categories of Irony Studied in Linguistic Literature
    1. A.1. Contradiction/false logic
    2. A.2. Metaphor
    3. A.3. Hyperbole/exaggeration
    4. A.4. Euphemism
    5. A.5. Absurdity
    6. A.6. Surprise
    7. A.7. Repetition
    8. A.8. Rhetorical question
    9. A.9. Register shift
    10. A.10. Oxymoron
    11. A.11. Paradox
    12. A.12. Quotation marks
    13. A.13. Emoticons
    14. A.14. Exclamation
    15. A.15. Capital letters, barred text and special characters
  11. References
  12. Index
  13. End User License Agreement
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