Automatic Sarcasm Detection: A Survey <s> INTRODUCTION <s> The robust understanding of sarcasm in a spoken dialogue system requires a reformulation of the dialogue manager’s basic assumptions behind, for example, user behavior and grounding strategies. But automatically detecting a sarcastic tone of voice is not a simple matter. This paper presents some experiments toward sarcasm recognition using prosodic, spectral, and contextual cues. Our results demonstrate that spectral and contextual features can be used to detect sarcasm as well as a human annotator would, and confirm a long-held claim in the field of psychology – that prosody alone is not sufficient to discern whether a speaker is being sarcastic. Index Terms: dialogue, user modeling, sarcasm, speech acts <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> INTRODUCTION <s> Irony is a fundamental rhetorical device. It is a uniquely human mode of communication, curious in that the speaker says something other than what he or she intends. Recently, computationally detecting irony has attracted attention from the natural language processing (NLP) and machine learning (ML) communities. While some progress has been made toward this end, I argue that current machine learning methods rely too heavily on shallow, unstructured, syntactic modeling of text to consistently discern ironic intent. Irony detection is an interesting machine learning problem because, in contrast to most text classification tasks, it requires a semantics that cannot be inferred directly from word counts over documents alone. To support this position, I survey the large body of existing philosophical/literary work investigating ironic communication. I then survey more recent computational efforts to operationalize irony detection in the fields of NLP and ML. I identify the disparities of the latter with respect to the former. Specifically, I highlight a major conceptual problem in all existing computational models of irony: none maintain an explicit model of the speaker/environment. I argue that without such an internal model of the speaker, irony detection is hopeless, as this model is necessary to represent expectations, which play a key role in ironic communication. I sketch possible means of embedding such models into computational approaches to irony detection. In particular, I introduce the pragmatic context model, which looks to operationalize computationally existing theories of irony. This work is a step toward unifying work on irony from literary, empirical and philosophical perspectives with modern computational models. <s> BIB002
The Free Dictionary 1 defines sarcasm as a form of verbal irony that is intended to express contempt or ridicule. The figurative nature of sarcasm makes it an often-quoted challenge for sentiment analysis  . Sarcasm has a negative implied sentiment, but may not have a negative surface sentiment. A sarcastic sentence may carry positive surface sentiment (for example, "Visiting dentists is so much fun! "), negative surface sentiment (for example, "His performance in Olympics has been terrible anyway" as a response to the criticism of an Olympic medalist), or no surface sentiment (for example, the idiomatic expression "and I am the Queen of England" 2 is used to express sarcasm). Since sarcasm implies sentiment, detection of sarcasm in a text is crucial to predicting the correct sentiment of the text. The challenges of sarcasm and the benefit of sarcasm detection to sentiment analysis have led to interest in automatic sarcasm detection as a research problem. Automatic sarcasm detection refers to computational approaches that predict if a given text is sarcastic. Thus, the sentence "I love it when my son rolls his eyes at me" should be predicted as sarcastic, while the sentence "I love it when my son gives me a present" should be predicted as non-sarcastic. This problem is difficult because of nuanced ways in which sarcasm may be expressed. However, sarcasm must be distinguished from humble bragging, as in the case of the sentence "I am having such a terrible holiday lying on the beach in the sunshine." Sarcasm and humble bragging are both situations where words are used to imply a sentiment that is different from their popular sentiment. Unlike humble bragging, a sarcastic sentence always has an implied negative sentiment because it intends to express contempt, as given in the definition.  However, to the best of our knowledge, past work does not typically distinguish between humble bragging and sarcasm and treats humble bragging as sarcasm. Starting with the earliest known work by Tepperman et al. BIB001 , which deals with sarcasm detection in speech, the area has seen wide interest from the sentiment analysis community. Sarcasm detection from text has now extended to different data forms and techniques. This synergy has resulted in interesting innovations for automatic sarcasm detection. The goal of this survey article is to provide a comprehensive summary of past work in computational sarcasm detection to enable new researchers to understand the state-of-the-art in this area. Reference BIB002 provides a comprehensive discussion of linguistic challenges of computational irony. The article focuses on linguistic theories and possible applications of these theories for sarcasm detection. We delve into the computational work. The rest of the article is organized as follows. We first describe sarcasm studies in linguistics in Section 2. To understand different aspects of the past work in automatic sarcasm detection, our article then looks at sarcasm detection in six steps. Section 3 presents different problem definitions. Sections 4, 5, and 6 describe datasets, approaches, and reported performance values, respectively. Section 7 highlights the trends underlying sarcasm detection, while Section 8 discusses recurring issues. Section 9 concludes the article and points to future work. This survey includes tables and illustrations that serve as useful pointers to obtain a perspective on sarcasm detection research. In addition, the descriptions of shared tasks and insights for future work may be useful for a researcher in sarcasm detection and related areas.