Automatic Sarcasm Detection: A Survey <s> Feature Sets <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> Feature Sets <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> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> The research described in this paper is focused on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused on describing a model for recognizing these phenomena in social media, such as ''tweets''. Our experiments are centered on five data sets retrieved from Twitter taking advantage of user-generated tags, such as ''#humor'' and ''#irony''. The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and figurative usages of language, are positive regarding humor, and encouraging regarding irony. <s> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <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> BIB004 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <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> BIB005 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> Irony is an important device in human communication, both in everyday spoken conversations as well as in written texts including books, websites, chats, reviews, and Twitter messages among others. Specific cases of irony and sarcasm have been studied in different contexts but, to the best of our knowledge, only recently the first publicly available corpus including annotations about whether a text is ironic or not has been published by Filatova (2012). However, no baseline for classification of ironic or sarcastic reviews has been provided. With this paper, we aim at closing this gap. We formulate the problem as a supervised classification task and evaluate different classifiers, reaching an F1-measure of up to 74 % using logistic regression. We analyze the impact of a number of features which have been proposed in previous research as well as combinations of them. <s> BIB006 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <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> BIB007 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language. We created a large Czech Twitter corpus consisting of 7,000 manually-labeled tweets and provide it to the community. We evaluate two classifiers with various combinations of features on both the Czech and English datasets. Furthermore, we tackle the issues of rich Czech morphology by examining different preprocessing techniques. Experiments show that our language-independent approach significantly outperforms adapted state-of-the-art methods in English (F-measure 0.947) and also represents a strong baseline for further research in Czech (F-measure 0.582). <s> BIB008 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> Irony, a creative use of language, has received scarce attention from the computational linguistics research point of view. We propose an automatic system capable of detecting irony with good accuracy in the social network Twitter. Twitter allows users to post short messages (140 characters) which usually do not follow the expected rules of the grammar, users tend to truncate words and use particular punctuation. For these reason automatic detection of Irony in Twitter is not trivial and requires specific linguistic tools. We propose in this paper a new set of experiments to assess the relevance of the features included in our model. Our model does not include words or sequences of words as features, aiming to detect inner characteristic of Irony. <s> BIB009 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from positive to negative). This paper presents an approach to address irony detection from a machine learning perspective. Our model considers structural features as well as, for the first time, sentiment analysis features such as the overall sentiment of a tweet and a score of its polarity. The approach has been evaluated over a set classifiers such as: Naive Bayes, Decision Tree, Maximum Entropy, Support Vector Machine, and for the first time in irony detection task: Multilayer Perceptron. The results obtained showed the ability of our model to distinguish between potentially ironic and non-ironic sentences. <s> BIB010 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <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> BIB011 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> Sarcasm is a nuanced form of language in which individuals state the opposite of what is implied. With this intentional ambiguity, sarcasm detection has always been a challenging task, even for humans. Current approaches to automatic sarcasm detection rely primarily on lexical and linguistic cues. This paper aims to address the difficult task of sarcasm detection on Twitter by leveraging behavioral traits intrinsic to users expressing sarcasm. We identify such traits using the user's past tweets. We employ theories from behavioral and psychological studies to construct a behavioral modeling framework tuned for detecting sarcasm. We evaluate our framework and demonstrate its efficiency in identifying sarcastic tweets. <s> BIB012 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <s> Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases. <s> BIB013 </s> Automatic Sarcasm Detection: A Survey <s> Feature Sets <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> BIB014
In this section, we investigate the set of features that have been reported for statistical sarcasm detection. Most approaches use bag-of-words as features. However, in addition to these, several other sets of features have been reported. Table 3 summarizes them. We focus on features related to the text to be classified. Contextual features (i.e., features that use information beyond the text to be classified) are described in a later section. Reference BIB001 designs pattern-based features that indicate the presence of discriminative patterns (such as "as fast as a snail") as extracted from a large sarcasm-labeled corpus. To prevent overfitting of patterns, these pattern-based features take real values based on three situations: exact match, partial overlap and no match. Reference BIB002 uses sentiment lexicon-based features and pragmatic features like emoticons and user mentions. Similarly, Reference BIB013 uses features derived from multiple affective lexicons such as AFINN, SentiWordNet, General Inquirer, and so  Freq. of rarest words, max/min/avg # synsets, max/min/avg # synonyms  Synonyms, Ambiguity, Written-spoken gap BIB006 Interjection, ellipsis, hyperbole, imbalance-based BIB007 POS sequences, Semantic imbalance. Chinese-specific features such as homophones, use of honorifics BIB008 Word shape, Pointedness, etc. BIB010 Length, capitalization, semantic similarity BIB011 Unigrams, Implicit incongruity-based, Explicit incongruity-based BIB012 Readability, sentiment flips, etc.  Pattern-based features along with word-based, syntactic, punctuation-based and sentiment-related features BIB013 Affect-based features derived from multiple emotion lexicons  Features based on word embedding similarity BIB014 Cognitive features derived from eye-tracking experiments on. In addition, they also use features based on semantic similarity, emoticons, counterfactuality, and so on. Reference BIB003 introduces features related to ambiguity, unexpectedness, emotional scenario, and so on. Ambiguity features cover structural, morpho-syntactic, semantic ambiguity, while unexpectedness features measure semantic relatedness. Reference BIB004 uses a set of patterns, specifically positive verbs and negative situation phrases, as features for a classifier (in addition to a rule-based classifier). Reference BIB005 introduces bigrams and trigrams as features. Reference  explores skip-gram and character n-gram-based features. Reference  includes seven sets of features such as maximum/minimum/gap of intensity of adjectives and adverbs, max/min/average number of synonyms and synsets for words in the target text, and so on. Reference BIB009 uses similar features for irony detection. Reference BIB006 incorporates ellipsis, hyperbole, and imbalance in their set of features. Reference BIB011 uses features corresponding to the linguistic theory of incongruity. The features are classified into two sets: implicit and explicit incongruity-based features. Reference BIB008 uses word-shape and pointedness features. Reference BIB012 uses extensions of words, number of flips, readability features in addition to contextual features. Reference BIB010 presents features that measure semantic relatedness between words using WordNet-based similarity. Reference BIB007 introduces POS sequences and semantic imbalance as features. Since they also experiment with Chinese datasets, they use language-typical features like use of homophony, use of honorifics, and so on. Following quite a different approach, salient features of sarcastic text were investigated by Reference BIB014 , which designed a set of gaze-based features such as average fixation duration, regression count, skip count, and so on, based on annotations from their eye-tracking experiments. In addition, they also use complex gaze-based features based on saliency graphs, which connect words in a sentence with edges representing saccades between the words.