Sentiment Analysis deals not only with the positive and negative sentiment detection in the text but it also considers the prevalence and challenges of sarcasm in sentiment-bearing text. Automatic Sarcasm detection deals with the detection of sarcasm in text. In the recent years, work in sarcasm detection gains popularity and has wide applicability in sentiment analysis. This paper complies the...

Sarcasm and irony, although similar, differ in that sarcasm has an impact on sentiment (because it is used to ridicule a target) while irony does not. Past work treats the two interchangeably. In this paper, we wish to validate if sarcasm versus irony classification is indeed a challenging task. To this end, we use a dataset of quotes from English literature, and conduct experiments from two pe...

Sarcasm Suite is a browser-based engine that deploys five of our past papers in sarcasm detection and generation. The sarcasm detection modules use four kinds of incongruity: sentiment incongruity, semantic incongruity, historical context incongruity and conversational context incongruity. The sarcasm generation module is a chatbot that responds sarcastically to user input. With a visually appe...

Sarcasm can radically alter or invert a phrase’s meaning. Sarcasm detection can therefore help improve natural language processing (NLP) tasks. The majority of prior research has modeled sarcasm detection as classification, with two important limitations: 1. Balanced datasets, when sarcasm is actually rather rare. 2. Using Twitter users’ self-declarations in the form of hashtags to label data, ...

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two...

thinking Psychological distance a b s t r a c t Sarcasm is ubiquitous in organizations. Despite its prevalence, we know surprisingly little about the cognitive experiences of sarcastic expressers and recipients or their behavioral implications. The current research proposes and tests a novel theoretical model in which both the construction and interpretation of sarcasm lead to greater creativit...

Sarcasm understandability or the ability to understand textual sarcasm depends upon readers’ language proficiency, social knowledge, mental state and attentiveness. We introduce a novel method to predict the sarcasm understandability of a reader. Presence of incongruity in textual sarcasm often elicits distinctive eye-movement behavior by human readers. By recording and analyzing the eye-gaze d...

Sarcasm detection is a recent innovation in sentiment analysis research. However, there has been no attention to sarcasm generation. We present a sarcasm-generation module for chatbots. The uniqueness of ‘SarcasmBot ’ is that it generates a sarcastic response for a user input. SarcasmBot is a sarcasm generation module that implements eight rule-based sarcasm generators, each of which generates ...

The authors explored the neurobiology of sarcasm and the cognitive processes underlying it by examining the performance of participants with focal lesions on tasks that required understanding of sarcasm and social cognition. Participants with prefrontal damage (n=25) showed impaired performance on the sarcasm task, whereas participants with posterior damage (n=16) and healthy controls (n=17) pe...

Sarcasm occurring due to the presence of numerical portions in text has been quoted as an error made by automatic sarcasm detection approaches in the past. We present a first study in detecting sarcasm in numbers, as in the case of the sentence ‘Love waking up at 4 am’. We analyze the challenges of the problem, and present Rulebased, Machine Learning and Deep Learning approaches to detect sarca...