#Irony or #Sarcasm — A Quantitative and Qualitative Study Based on Twitter

#Irony or #Sarcasm— A Quantitative and Qualitative Study Based on Twitter Po-Ya Angela Wang Graduate Institute of Linguistics, National Taiwan University, Taiwan 3F,Le-Xue Building, No.1, Sec.4,Roosevelt Road, Taipei Taiwan r01142009@ntu.edu.tw Abstract Current study is with the aim to identify similarities and distinctions between irony and sarcasm by adopting quantitative sentiment analysis as well as qualitative content analysis. The result of quantitative sentiment analysis shows that sarcastic tweets are used with more positive tweets than ironic tweets. The result of content analysis corresponds to the result of quantitative sentiment analysis in identifying the aggressiveness of sarcasm. On the other hand, from content analysis it shows that irony owns two senses. The first sense of irony is equal to aggressive sarcasm with speaker awareness. Thus, tweets of first sense of irony may attack a specific target, and the speaker may tag his/her tweet irony because the tweet itself is ironic. These tweets though tagged as irony are in fact sarcastic tweets. Different from this, the tweets of second sense of irony is tagged to classify an event to be ironic. However, from the distribution in sentiment analysis and examples in content analysis, irony seems to be more broadly used in its second sense. 1 Introduction Philosophers and rhetoricians have been interested in irony and sarcasm for over 2500 years (Katz, 2000). In recent years, irony and sarcasm have been popular issues discussed qualitatively and quantitatively. Being a special way of language creativity, irony and sarcasm provide the opportunity to explore the interaction between cognition and language. Many frameworks have been proposed to illustrate the mechanisms underlying, such as Echoic Mention (Sperber and Wilson, 1981, 1986), Echoic Reminder (Kreuz and Glucksberg, 1989), Allusional Pretense (Kumon-Nakamura et al., 1995), Pretense (Clark and Gerrig, 1984), Standard Pragmatic Model (Grice,1975; Searle, 1978), Traditional Oppositional Model reviewed by Clift (1999), and Framing and Footing (Clift, 1999). Empirical psycholinguistic studies are also conducted to understand what should be recognized as irony or sarcasm from hearer angle. Computational linguistics have devoted in related studies in order to develop refined machine program in detecting human’s real intention that is coated by used words. Though many insightful works have been done, current study is still with the intention to further explore this issue. The quantitative sentiment analysis and qualitative content analysis are adopted in probing the similarities and distinctions between irony and sarcasm. Current study provides another angle to understand irony and sarcasm by adopting both qualitative and quantitative methodology in exploring this issue from speakers’ performance in a different genre, internet language. 2 Literature Review Irony is a term used to describe unscrupulous trickery in its Greek term eironeia. Sarcasm, on the other hand, means to speak bitterly, and to tear flesh like dogs in its Greek word origin sarazein. (Katz, 2000). The two seem to be slightly different in their meanings from word origin; however, with the progress of language using, how these two terms reflect different cognition and language creativity as well as how language is achieved to …

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