Automatic Sarcasm Detection: A Survey <s> Miscellaneous Datasets <s> Speakers and listeners make use of a variety of pragmatic factors to produce and identify sarcastic statements. It is also possible that lexical factors play a role, although this possibility has not been investigated previously. College students were asked to read excerpts from published works that originally contained the phrase said sarcastically, although the word sarcastically was deleted. The participants rated the characters' statements in these excerpts as more likely to be sarcastic than those from similar excerpts that did not originally contain the word sarcastically. The use of interjections, such as gee or gosh, predicted a significant amount of the variance in the participants' ratings of sarcastic intent. This outcome suggests that sarcastic statements may be more formulaic than previously realized. It also suggests that computer software could be written to recognize such lexical factors, greatly increasing the likelihood that non-literal intent could be correctly interpreted by such programs, even if they are unable to identify the pragmatic components of nonliteral language. <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Miscellaneous Datasets <s> Irony is an effective but challenging mode of communication that allows a speaker to express sentiment-rich viewpoints with concision, sharpness and humour. Irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony --the simile --to show how irony is often marked in ways that make it computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes. <s> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Miscellaneous Datasets <s> Sarcasm is generally characterized as a figure of speech that involves the substitution of a literal by a figurative meaning, which is usually the opposite of the original literal meaning. We re-frame the sarcasm detection task as a type of word sense disambiguation problem, where the sense of a word is either literal or sarcastic. We call this the Literal/Sarcastic Sense Disambiguation (LSSD) task. We address two issues: 1) how to collect a set of target words that can have either literal or sarcastic meanings depending on context; and 2) given an utterance and a target word, how to automatically detect whether the target word is used in the literal or the sarcastic sense. For the latter, we investigate several distributional semantics methods and show that a Support Vector Machines (SVM) classifier with a modified kernel using word embeddings achieves a 7-10% F1 improvement over a strong lexical baseline. <s> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Miscellaneous Datasets <s> In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers. Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization, dialog systems and sentiment analysis is well recognized. Sarcasm can often be traced to incongruity that becomes apparent as the full sentence unfolds. This presence of incongruity- implicit or explicit- affects the way readers eyes move through the text. We observe the difference in the behaviour of the eye, while reading sarcastic and non sarcastic sentences. Motivated by this observation, we augment traditional linguistic and stylistic features for sarcasm detection with the cognitive features obtained from readers eye movement data. We perform statistical classification using the enhanced feature set so obtained. The augmented cognitive features improve sarcasm detection by 3.7% (in terms of Fscore), over the performance of the best reported system. <s> BIB004
In addition to the three kinds of datasets above, several other datasets have been reported. Reference BIB001 uses 20 sarcastic and 15 non-sarcastic book excerpts, which are marked by 101 annotators. The goal is to identify lexical indicators of sarcasm. Reference BIB002 focuses on identifying which similes are sarcastic. For example, the simile "as useful as a chocolate teapot" is to be predicted as sarcastic, while the simile "as big as a plum" is not.  Hence, they first search the web for the pattern "* as a *." This results in 20,000 distinct similes that are then annotated. Reference BIB003 uses a crowd-sourcing tool to obtain a non-sarcastic version of a sentence if applicable. For example, "Who doesn't love being ignored" is expected to be corrected to "Not many love being ignored." Reference BIB004 creates a manually labeled dataset of quotes from a website called sarcasmsociety.com. Reference  creates a similar dataset of quotes from GoodReads, a book recommendation website. However, in this case, they use user-determined tags to assign sarcasm labels.