Automatic Detection of Irony

1From Opinion Analysis to Figurative Language Treatment

1.1. Introduction

The first work on automatic opinion extraction (or opinion mining) dates back to the late 1990s, notably to (Hatzivassiloglou and McKeown 1997) seminal work on determining adjectival polarity in documents, i.e. identifying the positive or negative character of opinions expressed by these adjectives, and to (Pang et al. 2002) and (Littman and Turney 2002) work on classifying documents according to polarity.

Work on this subject has been in progress since the 2000s, and opinion extraction is one of the most active areas in both NLP and data mining, with over 26,000 publications identified by Google Scholar. Notable examples include (Wiebe et al. 2005) work on annotating the multi-perspective question answering (MPQA) opinion corpus, (Taboada et al. 2011) work on the effects of opinion operators, such as intensifiers, modalities and negations, and (Asher et al. 2009) and (Chardon et al. 2013) work on the use of the discursive structure in calculating the overall opinion expressed in a document. Finally, we note the emergence of a number of evaluation campaigns, such as the Text Retrieval Conference (TREC) (Ounis et al. 2008), the DEFT (Défi fouille de textes, data mining challenge) in French run for the first time in 2005 (Azé and Roche 2005), and the SemEval (Semantic Evaluation) campaign, started in 1998¹.

It is important to note that opinion analysis was already a subject of study in other domains, such ...