Automatic Sarcasm Detection: A Survey <s> Rule-Based Approaches <s> Irony is an effective but challenging mode of communication that allows a speaker to express sentiment-rich viewpoints with concision, sharpness and humour. Irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony --the simile --to show how irony is often marked in ways that make it computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes. <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Rule-Based Approaches <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> Rule-Based Approaches <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> Rule-Based Approaches <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
Rule-based approaches attempt to identify sarcasm through specific evidence. This evidence is captured in the form of rules that rely on indicators of sarcasm. Reference BIB001 identifies sarcasm in similes using Google searches to determine how likely a simile is. They present a nine-step approach where at each step/rule, a simile is validated as non-sarcastic using the number of search results. To demonstrate the strength of their rules, they present an error analysis corresponding to each rule. Reference BIB003 proposes that hashtag sentiment is a key indicator of sarcasm. Hashtags are often used by tweet authors to highlight sarcasm, and hence, if the sentiment expressed by a hashtag does not agree with rest of the tweet, the tweet is predicted as sarcastic. They use a hashtag tokenizer to split hashtags made of concatenated words. Reference BIB004 presents two rule-based classifiers. The first uses a parse-based lexicon generation algorithm that creates parse trees of sentences and identifies situation phrases that bear sentiment. If a negative phrase occurs in a positive sentence, then the sentence is predicted as sarcastic. The second algorithm aims to capture hyperbolic sarcasm (i.e., by using interjections (such as "wow" ) and intensifiers (such as "absolutely") that occur together. Reference BIB002 presents rule-based classifiers that look for a positive verb and a negative situation phrase in a sentence. The set of negative situation phrases are extracted using a wellstructured, iterative algorithm that begins with a bootstrapped set of positive verbs and iteratively expands both the sets (namely, positive verbs and negative situation phrases). They experiment with different configurations of rules such as restricting the order of the verb and situation phrase.