Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <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> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <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> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <s> Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans ::: too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. ::: While there exist a few systems which can detect sarcasm, almost no work has been carried out on studying the effect that sarcasm has on ::: sentiment in tweets, and on incorporating this into automatic tools for sentiment analysis. We perform an analysis of the effect of sarcasm ::: scope on the polarity of tweets, and have compiled a number of rules which enable us to improve the accuracy of sentiment analysis when ::: sarcasm is known to be present. We consider in particular the effect of sentiment and sarcasm contained in hashtags, and have developed ::: a hashtag tokeniser for GATE, so that sentiment and sarcasm found within hashtags can be detected more easily. According to our experiments, ::: the hashtag tokenisation achieves 98% Precision, while the sarcasm detection achieved 91% Precision and polarity detection 80%. <s> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <s> Sentiment Analysis is a technique to identify people's opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say (especially in order to insult or wit someone, to show irritation, or to be funny). People often expressed it verbally through the use of heavy tonal stress and certain gestural clues like rolling of the eyes. These tonal and gestural clues are obviously not available for expressing sarcasm in text, making its detection reliant upon other factors. In this paper, two approaches to detect sarcasm in the text of Twitter data were proposed. The first is a parsing-based lexicon generation algorithm (PBLGA) and the second was to detect sarcasm based on the occurrence of the interjection word. The combination of two approaches is also shown and compared with the existing state-of-the-art approach to detect sarcasm. First approach attains a 0.89, 0.81 and 0.84 precision, recall and f -- score respectively. Second approach attains 0.85, 0.96 and 0.90 precision, recall and f -- score respectively in tweets with sarcastic hashtag. <s> BIB004 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <s> Sarcasm understanding may require information beyond the text itself, as in the case of ‘I absolutely love this restaurant!’ which may be sarcastic, depending on the contextual situation. We present the first quantitative evidence to show that historical tweets by an author can provide additional context for sarcasm detection. Our sarcasm detection approach uses two components: a contrast-based predictor (that identifies if there is a sentiment contrast within a target tweet), and a historical tweet-based predictor (that identifies if the sentiment expressed towards an entity in the target tweet agrees with sentiment expressed by the author towards that entity in the past). <s> BIB005 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <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> BIB006 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <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> BIB007 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <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> BIB008 </s> Automatic Sarcasm Detection: A Survey <s> Issues with Sentiment as a Feature <s> Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not. <s> BIB009
Several approaches use lexical sentiment as a feature to the sarcasm classifier. It must, however, be noted that these approaches require 'surface polarity': the apparent polarity of a sentence. Reference BIB004 describes a rule-based approach that predicts a sentence as sarcastic if a negative phrase occurs in a positive sentence. As described earlier, Reference BIB005 uses sentiment of a past tweet by the author to predict sarcasm. In a statistical classifier, surface polarity may be used directly as a feature BIB006 BIB007 BIB008 BIB001 . Reference  captures polarity in terms of two emotion dimensions: activation and pleasantness. Reference BIB002 uses a sentiment imbalance feature that is represented by star rating of a review disagreeing with the surface polarity. Reference BIB009 cascades sarcasm detection and sentiment detection, and observes an improvement of 4% in accuracy for sentiment classification, when sentiment detection is aware of sarcastic nature of text. Reference BIB003 also demonstrates the impact of sarcasm detection on sentiment classification using modules from the GATE framework. Using simple rules to detect sarcasm expressed through hashtags, they improve the performance of sentiment classification by detecting sarcasm. It is important to note that Reference BIB003 observes that sarcasm may not always flip the polarity. The example they quote is: "Its not like I wanted to eat breakfast anyway. #sarcasm."