Automatic Detection of Irony

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).