Automatic Sarcasm Detection: A Survey <s> Pattern Discovery <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> Pattern Discovery <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> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Pattern Discovery <s> More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, monologic Natural Language Processing resources such as news, highly social dialogue is frequent in social media, making it a challenging context for NLP. This paper tests a bootstrapping method, originally proposed in a monologic domain, to train classifiers to identify two different types of subjective language in dialogue: sarcasm and nastiness. We explore two methods of developing linguistic indicators to be used in a first level classifier aimed at maximizing precision at the expense of recall. The best performing classifier for the first phase achieves 54% precision and 38% recall for sarcastic utterances. We then use general syntactic patterns from previous work to create more general sarcasm indicators, improving precision to 62% and recall to 52%. To further test the generality of the method, we then apply it to bootstrapping a classifier for nastiness dialogic acts. Our first phase, using crowdsourced nasty indicators, achieves 58% precision and 49% recall, which increases to 75% precision and 62% recall when we bootstrap over the first level with generalized syntactic patterns. <s> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Pattern Discovery <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> BIB004 </s> Automatic Sarcasm Detection: A Survey <s> Pattern Discovery <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> BIB005
Since sarcasm may contain implicit sentiment phrases (for example "just got off a wonderful 12 hour flight sitting next to a crying baby"), discovering sarcastic patterns was an early trend in sarcasm detection. Several approaches dealt with extracting patterns that are indicative of sarcasm, or carry implied sentiment. These patterns may then be used as features for a statistical classifier, or as a knowledge base for a rule-based classifier. Reference BIB001 extracts sarcastic patterns from a seed set of labeled sentences. They first select words that either occur more frequently than an upper threshold or less frequently than a lower threshold. They identify a large set of candidate patterns from among these extracted words. The patterns that occur discriminatively in either classes are then selected. References BIB004 and  also use a similar approach for Czech and English tweets. Reference BIB002 hypothesises that sarcasm occurs due to a contrast between positive verbs and phrases indicating negative situations (as in the case of "I love being awake at 4 am with a headache" where the positive verb "love" is followed by the negative situation "being awake at 4 am with a headache"). To discover a lexicon of these verbs and phrases, the authors design an iterative algorithm. Starting with a seed set of positive verbs, they identify discriminative situation phrases that occur with these verbs in sarcastic tweets. These phrases are then used to expand the set of verbs. The algorithm iteratively appends to the lists of known verbs and phrases. The algorithm also incorporates subsumption of shorter situation phrases by longer situation phrases. For example, "being awake at 4 am" as in the previous example has an implicit negative sentiment in itself as compared to the longer phrase "being awake at 4am with a headache." Reference BIB005 adapts this algorithm by eliminating subsumption. Reference BIB003 begins with a seed set of nastiness and sarcasm patterns, created using Amazon Mechanical Turk. These nastiness/sarcasm patterns are statistically significant patterns (such as "oh really") derived from a labeled dataset. They train a high precision sarcastic post classifier, followed by a high precision non-sarcastic post classifier. These two classifiers are then used to generate a large labeled dataset, which is in turn used to train a classifier.