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

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).

Preface ix

Introduction xi

Chapter 1. From Opinion Analysis to Figurative Language Treatment 1

1.1. Introduction 1

1.2. Defining the notion of opinion 3

1.2.1. The many faces of opinion 3

1.2.2. Opinion as a structured model 4

1.2.3. Opinion extraction: principal approaches 5

1.3. Limitations of opinion analysis systems 7

1.3.1. Opinion operators 8

1.3.2. Domain dependency 9

1.3.3. Implicit opinions 10

1.3.4. Opinions and discursive context above phrase level 11

1.3.5. Presence of figurative expressions 12

1.4. Definition of figurative language 13

1.4.1. Irony 13

1.4.2. Sarcasm 18

1.4.3. Satire 20

1.4.4. Metaphor 21

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