Automatic Sarcasm Detection: A Survey <s> CONCLUSION & FUTURE DIRECTIONS <s> 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. <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> CONCLUSION & FUTURE DIRECTIONS <s> Sarcasm is a pervasive linguistic phenomenon in online documents that express subjective and deeply-felt opinions. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Current studies consider automatic sarcasm detection as a simple text classification problem. They do not use explicit features to detect sarcasm and ignore the imbalance between sarcastic and non-sarcastic samples in real applications. In this paper, we first explore the characteristics of both English and Chinese sarcastic sentences and introduce a set of features specifically for detecting sarcasm in social media. Then, we propose a novel multi-strategy ensemble learning approach(MSELA) to handle the imbalance problem. We evaluate our proposed model on English and Chinese data sets. Experimental results show that our ensemble approach outperforms the state-of-the-art sarcasm detection approaches and popular imbalanced classification methods. <s> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> CONCLUSION & FUTURE DIRECTIONS <s> The relationship between context incongruity and sarcasm has been studied in linguistics. We present a computational system that harnesses context incongruity as a basis for sarcasm detection. Our statistical sarcasm classifiers incorporate two kinds of incongruity features: explicit and implicit. We show the benefit of our incongruity features for two text forms tweets and discussion forum posts. Our system also outperforms two past works (with Fscore improvement of 10-20%). We also show how our features can capture intersentential incongruity. <s> BIB003
Sarcasm detection research has grown significantly in the past few years, necessitating that we look back and assess the overall picture that these individual works have led to. This article surveys approaches to automatic sarcasm detection. We observed three milestones in the history of sarcasm detection research: semi-supervised pattern extraction to identify implicit sentiment, the use of hashtag-based supervision to create large-scale annotated datasets, and the use of context beyond target text. The article presented illustrations containing the datasets, approaches, and performance values, as reported in past work. Manually labeled datasets and datasets labeled using distant supervision are two popular techniques for creation of sarcasm-labeled datasets. We observed that rule-based approaches capture evidence of sarcasm in the form of rules, such as that the sentiment of the hashtag does not match the sentiment of the rest of the tweet. Statistical approaches use features like sentiment changes, specific semi-supervised patterns, and so on. Some deep learning approaches have also been reported. To incorporate context, additional features specific to the author, the conversation, and the topic have been explored in the past. An underlying theme of these past approaches (either in terms of rules or features) is attempting to identify the "irony" and "hurtful nature" that is at the source of sarcasm. We also highlight three issues in sarcasm detection: the quality of sarcasm annotation through manual or distant supervised datasets, the relationship between sarcasm and sentiment as a feature, and data skew in the case of sarcasm-labeled datasets. Our table that compares key past articles along dimensions such as approach, annotation approach, features, and so on, should be useful to those trying to understand the current state-of-art in sarcasm detection research. The current trend of research in sarcasm detection points to the discovery of new features and the incorporation of context. While there are manually labeled gold-standard datasets, using distant supervision to obtain labels is the prominent approach. Novel techniques to incorporate context of different forms have also been employed. Based on our survey of these works, we observe the following emerging directions: (1) Quality of sarcasm annotation: Sarcasm is understood on the basis of shared knowledge. As shown in Reference BIB002 , sarcasm is closely related to language/culture-specific traits. Future approaches to sarcasm detection in new languages will benefit from understanding such traits and incorporating them into their classification frameworks. Reference  shows that American and Indian annotators may have substantial disagreement in their sarcasm annotations. However, this sees a non-significant degradation in the performance of sarcasm detection. Since crowd-sourcing may be used for sarcasm annotation, the quality of this annotation and its impact on sarcasm classification must be evaluated on the basis of critical parameters such as cultural backgrounds. (2) Extraction of implicit sentiment in patterns: Based on past work, it is well-established that sarcasm is closely linked to sentiment incongruity BIB003 . Several related works exist for detection of implicit sentiment in sentences, as in the case of "The phone gets heated quickly" versus "The induction cooktop gets heated quickly." This will help sarcasm detection, following the line of semi-supervised pattern discovery. (3) Analysis based on types of sarcasm: As noted in the survey, past work does not report which of the types of sarcasm are correctly handled by existing systems. A dataset that labels sarcastic sentences into one of the four types and then studies the performance of various systems on each of these types will be helpful. Future work can benefit from reporting which types of sarcasm are proving to be difficult for different approaches. (4) Sarcasm versus irony classification: Sarcasm and irony are closely related and most work so far considers them to be the same. However, some recent work has dealt with understanding the differences between the two. Reference BIB001 presents findings of a data analysis to understand differences between sarcasm and irony. According to the authors, aggressiveness is the distinguishing factor between the two. Reference  presents a set of classifiers that distinguish between sarcasm and irony. The authors describe an analysis of structural and affective features in tweets. An important observation that they make is the peculiarity of the hashtag "#not" as a negation marker for sarcasm. (5) Linguistic basis for sarcasm detection: Many sarcasm theories, except the theory of dropped negation (described in Section 2), have not been explored as means for sarcasm detection. Reference  shows that the hashtag "#not" plays a distinct role in sarcastic tweets. This may have correlations with this theory of dropped negation. Approaches grounded in linguistic theories may yield good results. (6) Coverage of different forms of sarcasm: In Section 2, we described four species of sarcasm: propositional, lexical, like-prefixed, and illocutionary sarcasm. We observe that current approaches are limited in handling the last two forms of sarcasm: like-prefixed and illocutionary. Future work may focus on these forms of sarcasm.