Sarcasm is a nuanced form of language where usually, the speaker explicitly states the opposite of what is implied. Imbued with intentional ambiguity and subtlety, detecting sarcasm is a difficult task, even for humans. Current works approach this challenging problem primarily from a linguistic perspective, focussing on the lexical and syntactic aspects of sarcasm. In this thesis, I explore the...

Sarcasm is a form of verbal irony that is intended to express contempt or ridicule. Often quoted as a challenge to sentiment analysis, sarcasm involves use of words of positive or no polarity to convey negative sentiment. Incongruity has been observed to be at the heart of sarcasm understanding in humans. Our work in sarcasm detection identifies different forms of incongruity and employs differ...

The understanding of sarcasm reflects a complex process, which involves recognizing the beliefs of the speaker. There is a clear association between deficits in mentalizing, which is the ability to understand other people's behavior in terms of their mental state, and the understanding of sarcasm in individuals with autistic spectrum disorders. This suggests that mentalizing is important in pra...

Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. While there exist a few systems which can detect sarcasm, almost no work has been carried out on studying the effect that s...

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements — 10 times more than any previous dataset — and many times more instances of non-sarcastic statements, allowing for learning in both balanced and unbalanced label regimes. Each statement is furthe...

The goal of this study was to identify acoustic parameters associated with the expression of sarcasm by Cantonese speakers, and to compare the observed features to similar data on English [Cheang, H. S. and Pell, M. D. (2008). Speech Commun. 50, 366-381]. Six native Cantonese speakers produced utterances to express sarcasm, humorous irony, sincerity, and neutrality. Each utterance was analyzed ...

Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identification (i.e., extracting the target of ridicule in a sarcastic sentence). We present an introductory approach for sarcasm target identification. Our approach employs two types of extractors: one based on rules, and another consisting of a statis...

Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very s...

The present study was conducted to identify possible acoustic cues of sarcasm. Native English speakers produced a variety of simple utterances to convey four different attitudes: sarcasm, humour, sincerity, and neutrality. Following validation by a separate naı̈ve group of native English speakers, the recorded speech was subjected to acoustic analyses for the following features: mean fundamental...

Violence and aggression in the workplace are problems that most Americans confront on a daily basis. The present study is an exploration of the predisposition to conflict in a work environment in which personality traits responsible for increased sarcasm and increased anger in response to sarcasm are identified. Participants represented two subdepartments within a city general hospital. The Kei...