Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <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> Learning Algorithms <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> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> 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 stages: semisupervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks. <s> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> Sarcasm transforms the polarity of an apparently positive or negative utterance into its opposite. We report on a method for constructing a corpus of sarcastic Twitter messages in which determination of the sarcasm of each message has been made by its author. We use this reliable corpus to compare sarcastic utterances in Twitter to utterances that express positive or negative attitudes without sarcasm. We investigate the impact of lexical and pragmatic factors on machine learning effectiveness for identifying sarcastic utterances and we compare the performance of machine learning techniques and human judges on this task. Perhaps unsurprisingly, neither the human judges nor the machine learning techniques perform very well. <s> BIB004 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony, in particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents that trigger a chain reaction in people. The findings were assessed employing three classifiers. Initial results are largely positive, and provide valuable insights into the subjective issues of language facing tasks such as sentiment analysis, opinion mining and decision making. <s> BIB005 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> A common form of sarcasm on Twitter consists of a positive sentiment contrasted with a negative situation. For example, many sarcastic tweets include a positive sentiment, such as “love” or “enjoy”, followed by an expression that describes an undesirable activity or state (e.g., “taking exams” or “being ignored”). We have developed a sarcasm recognizer to identify this type of sarcasm in tweets. We present a novel bootstrapping algorithm that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets. We show that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition. <s> BIB006 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> To avoid a sarcastic message being understood in its unintended literal meaning, in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with the hashtag ‘#sarcasm’. We collected a training corpus of about 78 thousand Dutch tweets with this hashtag. Assuming that the human labeling is correct (annotation of a sample indicates that about 85% of these tweets are indeed sarcastic), we train a machine learning classifier on the harvested examples, and apply it to a test set of a day’s stream of 3.3 million Dutch tweets. Of the 135 explicitly marked tweets on this day, we detect 101 (75%) when we remove the hashtag. We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag. 30% of the top-250 ranked tweets are indeed sarcastic. Analysis shows that sarcasm is often signalled by hyperbole, using intensifiers and exclamations; in contrast, non-hyperbolic sarcastic messages often receive an explicit marker. We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of nonverbal expressions that people employ in live interaction when conveying sarcasm. <s> BIB007 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <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> BIB008 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <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> BIB009 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance on Twitter — such as properties of the author, the audience and the immediate communicative environment — we are able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation. <s> BIB010 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> Automatically detecting sarcasm in twitter is a challenging task because sarcasm transforms the polarity of an apparently positive or negative utterance into its opposite. Previous work focus on feature modeling of the single tweet, which limit the performance of the task. These methods did not leverage contextual information regarding the author or the tweet to improve the performance of sarcasm detection. However, tweets are filtered through streams of posts, so that a wider context, e.g. a conversation or topic, is always available. In this paper, we compared sarcastic utterances in twitter to utterances that express positive or negative attitudes without sarcasm. The sarcasm detection problem is modeled as a sequential classification task over a tweet and his contextual information. A Markovian formulation of the Support Vector Machine discriminative model as embodied by the $$SVM^{hmm}$$ algorithm has been employed to assign the category label to entire sequence. Experimental results show that sequential classification effectively embodied evidence about the context information and is able to reach a relative increment in detection performance. <s> BIB011 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manuallylabeled dataset of dialogue from TV series ‘Friends’ annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance gain using sequence labeling as compared to classification-based approaches. Our experiments are based on three sets of features, one is derived from information in our dataset, the other two are from past works. Two sequence labeling algorithms (SVM-HMM and SEARN) outperform three classification algorithms (SVM, Naive Bayes) for all these feature sets, with an increase in F-score of around 4%. Our observations highlight the viability of sequence labeling techniques for sarcasm detection of dialogue. <s> BIB012 </s> Automatic Sarcasm Detection: A Survey <s> Learning Algorithms <s> Abstract Sarcasm detection of online text is a task of growing importance in the globalized world. Large corporations are interested in knowing how consumers perceive the various products launched by the companies based on analysis of microblogs, such as - Twitter, about their products.These reviews/comments/posts are under the constant threat of being classified in the wrong category due to use of sarcasm in sentences. Automatic detection of sarcasm in microblogs, such as - Twitter, is a difficult task. It requires a system that can use some knowledge to interpret the linguistic styles of authors. In this work, we try to provide this knowledge to the system by considering different sets of features which are relatively independent of the text, namely - function words and part of speech n-grams. We test a range of different feature sets using the Naive Bayes and fuzzy clustering algorithms. Our results show that the sarcasm detection task benefits from the inclusion of features which capture authorial style of the microblog authors. We achieve an accuracy of approximately 65% which is on the higher side of the sarcasm detection literature. <s> BIB013
Most work in statistical sarcasm detection relies on different forms of Support Vector Machines (SVM)  BIB009 BIB002 BIB005 BIB001 BIB003 (or SVM-Perf as in the case of Reference  ). Reference BIB004 uses SVM and Logistic Regression, with the χ 2 test used to identify discriminating features. Reference BIB006 compares rule-based techniques with a SVM-based classifier. Reference BIB007 uses the balanced winnow algorithm to determine high-ranking features. Reference  uses Naive Bayes and Decision Trees for multiple pairs of labels among irony, humor, politics and education. Reference BIB010 uses binary Logistic Regression. Reference BIB011 uses SVM-HMM to incorporate sequence nature of output labels in a conversation. Similarly, Reference BIB012 validates that for conversational data, sequence labeling algorithms perform better than classification algorithms. They use SVM-HMM and SEARN as the sequence labeling algorithms. Reference BIB008 compare several ensemble-based classification approaches including Bagging, Boosting, and so on, and show results on five datasets. Reference BIB013 uses fuzzy Clustering for sarcasm detection.