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…mehr
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).Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jihen Karoui is Research and Development Project Manager at AUSY, France. Farah Benamara is a Senior Lecturer at Paul Sabatier University in Toulouse, France. Véronique Moriceau is a Senior Lecturer at Paul Sabatier University.
Inhaltsangabe
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 1.4.5. Humor 22 1.5. Figurative language: a challenge for NLP 23 1.6. Conclusion 23 Chapter 2. Toward Automatic Detection of Figurative Language 25 2.1. Introduction 25 2.2. The main corpora used for figurative language 27 2.2.1. Corpora annotated for irony/sarcasm 28 2.2.2. Corpus annotated for metaphors 33 2.3. Automatic detection of irony, sarcasm and satire 36 2.3.1. Surface and semantic approaches 36 2.3.2. Pragmatic approaches 39 2.4. Automatic detection of metaphor 51 2.4.1. Surface and semantic approaches 52 2.4.2. Pragmatic approaches 53 2.5. Automatic detection of comparison 58 2.6. Automatic detection of humor 58 2.7. Conclusion 61 Chapter 3. A Multilevel Scheme for Irony Annotation in Social Network Content 63 3.1. Introduction 63 3.2. The FrIC 65 3.3. Multilevel annotation scheme 66 3.3.1. Methodology 66 3.3.2. Annotation scheme 69 3.4. The annotation campaign 79 3.4.1. Glozz 79 3.4.2. Data preparation 80 3.4.3. Annotation procedure 81 3.5. Results of the annotation campaign 83 3.5.1. Qualitative results 83 3.5.2. Quantitative results 84 3.5.3. Correlation between different levels of the annotation scheme 89 3.6. Conclusion 93 Chapter 4. Three Models for Automatic Irony Detection 95 4.1. Introduction 95 4.2. The FrICAuto corpus 97 4.3. The SurfSystem model: irony detection based on surface features 99 4.3.1. Selected features 99 4.3.2. Experiments and results 101 4.4. The PragSystem model: irony detection based on internal contextual features 104 4.4.1. Selected features 104 4.4.2. Experiments and results 109 4.4.3. Discussion 116 4.5. The QuerySystem model: developing a pragmatic contextual approach for automatic irony detection 118 4.5.1. Proposed approach 118 4.5.2. Experiments and results 122 4.5.3. Evaluation of the query-based method 123 4.6. Conclusion 124 Chapter 5. Towards a Multilingual System for Automatic Irony Detection 127 5.1. Introduction 127 5.2. Irony in Indo-European languages 128 5.2.1. Corpora 128 5.2.2. Results of the annotation process 130 5.2.3. Summary 139 5.3. Irony in Semitic languages 140 5.3.1. Specificities of Arabic 142 5.3.2. Corpus and resources 143 5.3.3. Automatic detection of irony in Arabic tweets 146 5.4. Conclusion 149 Conclusion 151 Appendix 155 References 169 Index 189
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 1.4.5. Humor 22 1.5. Figurative language: a challenge for NLP 23 1.6. Conclusion 23 Chapter 2. Toward Automatic Detection of Figurative Language 25 2.1. Introduction 25 2.2. The main corpora used for figurative language 27 2.2.1. Corpora annotated for irony/sarcasm 28 2.2.2. Corpus annotated for metaphors 33 2.3. Automatic detection of irony, sarcasm and satire 36 2.3.1. Surface and semantic approaches 36 2.3.2. Pragmatic approaches 39 2.4. Automatic detection of metaphor 51 2.4.1. Surface and semantic approaches 52 2.4.2. Pragmatic approaches 53 2.5. Automatic detection of comparison 58 2.6. Automatic detection of humor 58 2.7. Conclusion 61 Chapter 3. A Multilevel Scheme for Irony Annotation in Social Network Content 63 3.1. Introduction 63 3.2. The FrIC 65 3.3. Multilevel annotation scheme 66 3.3.1. Methodology 66 3.3.2. Annotation scheme 69 3.4. The annotation campaign 79 3.4.1. Glozz 79 3.4.2. Data preparation 80 3.4.3. Annotation procedure 81 3.5. Results of the annotation campaign 83 3.5.1. Qualitative results 83 3.5.2. Quantitative results 84 3.5.3. Correlation between different levels of the annotation scheme 89 3.6. Conclusion 93 Chapter 4. Three Models for Automatic Irony Detection 95 4.1. Introduction 95 4.2. The FrICAuto corpus 97 4.3. The SurfSystem model: irony detection based on surface features 99 4.3.1. Selected features 99 4.3.2. Experiments and results 101 4.4. The PragSystem model: irony detection based on internal contextual features 104 4.4.1. Selected features 104 4.4.2. Experiments and results 109 4.4.3. Discussion 116 4.5. The QuerySystem model: developing a pragmatic contextual approach for automatic irony detection 118 4.5.1. Proposed approach 118 4.5.2. Experiments and results 122 4.5.3. Evaluation of the query-based method 123 4.6. Conclusion 124 Chapter 5. Towards a Multilingual System for Automatic Irony Detection 127 5.1. Introduction 127 5.2. Irony in Indo-European languages 128 5.2.1. Corpora 128 5.2.2. Results of the annotation process 130 5.2.3. Summary 139 5.3. Irony in Semitic languages 140 5.3.1. Specificities of Arabic 142 5.3.2. Corpus and resources 143 5.3.3. Automatic detection of irony in Arabic tweets 146 5.4. Conclusion 149 Conclusion 151 Appendix 155 References 169 Index 189
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