The past work in sarcasm detection involves rule-based and statistical approaches using: (a) unigrams and pragmatic features (such as emoticons, etc.) (Gonzalez-Ibanez et al., 2011; Carvalho et al., 2009; Barbieri et al., 2014) , (b) extraction of common patterns, such as hashtag-based sentiment (Maynard and Greenwood, 2014; Liebrecht et al., 2013) , a positive verb being followed by a negative situation<cite> (Riloff et al., 2013)</cite> , or discriminative n-grams (Tsur et al., 2010a; Davidov et al., 2010) .