Automatic Sarcasm Detection: A Survey <s> Dealing with Skewed Datasets <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> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Dealing with Skewed Datasets <s> While a fair amount of work has been done on automatically detecting emotion in human speech, there has been little research on sarcasm detection. Although sarcastic speech acts are inherently subjective, humans have relatively clear intuitions as to what constitutes sarcastic speech. In this paper, we present a system for automatic sarcasm detection. Using a new acted speech corpus that is annotated for sarcastic and sincere speech, we examine a number of features that are indicative of sarcasm. The first set of features looks at a baseline of basic acoustic features that have been found to be helpful in human sarcasm identification. We then present an effective way of modeling and applying prosodic contours to the task of automatic sarcasm detection. This approach applies sequential modeling to categorical representations of pitch and intensity contours obtained via k-means clustering. Using a SimpleLogistic (LogitBoost) classifier, we are able to predict sarcasm with 81.57% accuracy. This result suggests that certain pitch and intensity contours are predictive of sarcastic speech. <s> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Dealing with Skewed Datasets <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> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Dealing with Skewed Datasets <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> BIB004 </s> Automatic Sarcasm Detection: A Survey <s> Dealing with Skewed Datasets <s> Automatically detecting verbal irony (roughly, sarcasm) in online content is important for many practical applications (e.g., sentiment detection), but it is difficult. Previous approaches have relied predominantly on signal gleaned from word counts and grammatical cues. But such approaches fail to exploit the context in which comments are embedded. We thus propose a novel strategy for verbal irony classification that exploits contextual features, specifically by combining noun phrases and sentiment extracted from comments with the forum type (e.g., conservative or liberal) to which they were posted. We show that this approach improves verbal irony classification performance. Furthermore, because this method generates a very large feature space (and we expect predictive contextual features to be strong but few), we propose a mixed regularization strategy that places a sparsity-inducing `1 penalty on the contextual feature weights on top of the `2 penalty applied to all model coefficients. This increases model sparsity and reduces the variance of model performance. <s> BIB005 </s> Automatic Sarcasm Detection: A Survey <s> Dealing with Skewed Datasets <s> 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, when sarcasm can take many forms. To address these issues, we create an unbalanced corpus of manually annotated Twitter conversations. We compare human and machine ability to recognize sarcasm on this data under varying amounts of context. Our results indicate that both class imbalance and labelling method affect performance, and should both be considered when designing automatic sarcasm detection systems. We conclude that for progress to be made in real-world sarcasm detection, we will require a new class labelling scheme that is able to access the ‘common ground’ held between conversational parties. <s> BIB006
Sarcasm is an infrequent phenomenon of sentiment expression. This skew also reflects in datasets. Reference BIB001 uses a dataset with a small set of sentences are marked as sarcastic. 12.5% of tweets in the Italian dataset given by Reference  are sarcastic. On the other hand, Reference BIB002 presents a balanced dataset of 15k tweets. In some articles, specialized techniques are used to deal with the dataset imbalances. For example, Reference BIB004 presents a multi-strategy ensemble learning approach. Reference  uses SVM-perf that performs F-score optimization. Similarly, to deal with sparse features and skewness of data, Reference BIB005 introduce an LSS-regularization strategy. They use a sparsifying L1 regularizer over contextual features and L2-norm for bag of word features. Data imbalance also influences the choice of performance metrics reported. Since AUC is known to be a more reliable indicator of performance than F-score for skewed data, Reference BIB003 reports AUC for balanced as well as skewed datasets, to demonstrate the benefit of their classifier. Another methodology to ascertain benefit of a given approach withstanding data skew is by Reference BIB006 . The authors compare performance of sarcasm classification for many datasets of different data imbalances.