Clues for detecting irony in user-generated contents

Abstract

We investigate the accuracy of a set of surface patterns in identifying ironic sentences in comments submitted by users to an on-line newspaper. The initial focus is on identifying irony in sentences containing positive predicates since these sentences are more exposed to irony, making their true polarity harder to recognize. We show that it is possible to find ironic sentences with relatively high precision (from 45% to 85%) by exploring certain oral or gestural clues in user comments, such as emoticons, onomatopoeic expressions for laughter, heavy punctuation marks, quotation marks and positive interjections. We also demonstrate that clues based on deeper linguistic information are relatively inefficient in capturing irony in user-generated content, which points to the need for exploring additional types of oral clues.

Computational rule-based model for Irony Detection in Italian Tweets

“…Moreover, this research highlights the importance of surface traits in ironic texts, such as: punctuation marks (González-Ibáñez et al, 2011), sequence or combination of exclamation and question marks (Carvalho et al, 2009;Buschmeier et al, 2014), tweet length (Davidov et al, 2010), interjections (González-Ibáñez et al, 2011), words in capital letters (Reyes et al, 2013), emoticons (Buschmeier et al, 2014), quotations (Tsur et al, 2010) and opposition words, as "but" or "although" (Utsumi, 2004) 6 . Carvalho et al (2009) distinguished eight "clues" for irony detection in some comments (each consisting of about four sentences) from a Portuguese online newspaper. Their attention focused on positive comments because in a previous research they showed that positive sentences are more subjected to irony and it is more difficult to recognize their true polarity.…”

“…In particular we focus on the ironic emoticons, those which express joking or ironic intention (see section 4.1). We have distinguished EMOIRO from EMOPOS because positive emoticons (considered in Carvalho et al, 2009 andGonzález-Ibáñez et al, 2011) are frequently used to express a humorous intention, not specifically ironic.…”

Good location, terrible food: detecting feature sentiment in user-generated reviews

“…Notably, users often misspell words, use idiosyncratic spelling variants, and avoid standard grammatical structures, resulting in uninterpretable dependency graphs. Moreover, opinions can be presented through irony and sarcasm, causing mistakes in the sentiment analysis (Carvalho et al 2009). In international open platforms, users express themselves in a variety of native and non-native languages, resulting in complex, non-standard mixtures of linguistic styles that defy traditional natural language processing tools (Warschauer et al 2010).…”

Exploring the fine-grained analysis and automatic detection of irony on Twitter

“…It can be observed that a number of features are the same across languages, such as bags of words, PoS patterns and sentiment features. However, some features do capture language-specific characteristics of irony, such as the frequent use of negations in French (Karoui et al 2015), variations in verb morphology and cross-constructions in Portuguese (Carvalho et al 2009), political discourse in Italian (Barbieri et al 2014), words that showcase figurative language in Greek (Charalampakis et al 2016) and words whose polarity varies depending on its context in Indonesian (e.g. the Indonesian word for 'student' means 'low price' in particular contexts) (Lunando and Purwarianti 2015).…”