Modelling Sarcasm in Twitter, a Novel Approach

Abstract

Automatic detection of figurative language is a challenging task in computational linguistics. Recognising both literal and figurative meaning is not trivial for a machine and in some cases it is hard even for humans. For this reason novel and accurate systems able to recognise figurative languages are necessary. We present in this paper a novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages). Our model is easy to implement and, unlike previous systems, it does not include patterns of words as features. Our seven sets of lexical features aim to detect sarcasm by its inner structure (for example unexpectedness, intensity of the terms or imbalance between registers), abstracting from the use of specific terms.

Irony Detection in Twitter

“…Experimental results show that emotIDM outperforms the irony detection models presented in [Riloff et al 2013;Reyes et al 2013;Barbieri et al 2014;Hernández Farías et al 2015] over the same datasets.…”

“…#irony, #sarcasm, #sarcastic, #not) have been considered as ironic utterances. Following this framework, different approaches have been proposed [Davidov et al 2010;González-Ibáñez et al 2011;Reyes et al 2013;Riloff et al 2013;Barbieri et al 2014;Ptáček et al 2014;Hernández Farías et al 2015;Fersini et al 2015]. The authors proposed models that exploit mainly textual-content such as: punctuation marks, emoticons, part-of-speech labels, discursive terms, specific patterns (e.g., according to [Riloff et al 2013], a common form of sarcasm in Twitter consists of a positive sentiment contrasting with a negative situation), among others.…”

“…Indeed, Twitter users have to be sharp and short, having only 140 characters for expressing their comments, and most of the times the ironic posts do not require knowledge about the conversational context to be understood. Several works have been carried out using tweets for experimental purposes [Davidov et al 2010;González-Ibáñez et al 2011;Reyes et al 2013;Wang 2013;Riloff et al 2013;Barbieri et al 2014;Ptáček et al 2014;Hernández Farías et al 2015;Rajadesingan et al 2015;Bamman and Smith 2015;Joshi et al 2015;Karoui et al 2015]. Furthermore, there are some efforts in other so-cial media such as customer reviews from Amazon 1 [Filatova 2012;Buschmeier et al 2014]; comments from the online debate sites such as 4forums.com 2 [Abbott et al 2011;Lukin and Walker 2013] and, recently, Reddit 3 .…”

“…There is now a consistent body of work on computational models for irony and sarcasm detection in social media [González-Ibáñez et al 2011;Reyes et al 2013;Wang 2013;Riloff et al 2013;Barbieri et al 2014;Ptáček et al 2014;Hernández Farías et al 2015], and in particular in Twitter, which can be considered the most widely used source of information to experiment with irony detection. In this article we also address the task of detecting irony in tweets, by identifying a set of discriminative features to automatically differentiate an ironic text from a non-ironic one.…”

ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

“…In (Riloff et al, 2013) the focus is on identifying sarcastic tweets that express a positive sentiment towards a negative situation. A model to classify sarcastic tweets using a set of lexical features is presented in (Barbieri et al, 2014). Moreover, a recent analysis on the interplay between sarcasm detection and sentiment analysis is in (May-nard and Greenwood, 2014), where a set of rules has been proposed to improve the performance of the sentiment analysis in presence of sarcastic tweets.…”