Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <s> Automatically detecting verbal irony (roughly, sarcasm) is a challenging task because ironists say something other than ‐ and often opposite to ‐ what they actually mean. Discerning ironic intent exclusively from the words and syntax comprising texts (e.g., tweets, forum posts) is therefore not always possible: additional contextual information about the speaker and/or the topic at hand is often necessary. We introduce a new corpus that provides empirical evidence for this claim. We show that annotators frequently require context to make judgements concerning ironic intent, and that machine learning approaches tend to misclassify those same comments for which annotators required additional context. <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <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> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <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> BIB003 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <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> BIB004 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <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 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <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> BIB006 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <s> Automatically detecting sarcasm in twitter is a challenging task because sarcasm transforms the polarity of an apparently positive or negative utterance into its opposite. Previous work focus on feature modeling of the single tweet, which limit the performance of the task. These methods did not leverage contextual information regarding the author or the tweet to improve the performance of sarcasm detection. However, tweets are filtered through streams of posts, so that a wider context, e.g. a conversation or topic, is always available. In this paper, we compared sarcastic utterances in twitter to utterances that express positive or negative attitudes without sarcasm. The sarcasm detection problem is modeled as a sequential classification task over a tweet and his contextual information. A Markovian formulation of the Support Vector Machine discriminative model as embodied by the $$SVM^{hmm}$$ algorithm has been employed to assign the category label to entire sequence. Experimental results show that sequential classification effectively embodied evidence about the context information and is able to reach a relative increment in detection performance. <s> BIB007 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <s> We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features. <s> BIB008 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <s> This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manuallylabeled dataset of dialogue from TV series ‘Friends’ annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance gain using sequence labeling as compared to classification-based approaches. Our experiments are based on three sets of features, one is derived from information in our dataset, the other two are from past works. Two sequence labeling algorithms (SVM-HMM and SEARN) outperform three classification algorithms (SVM, Naive Bayes) for all these feature sets, with an increase in F-score of around 4%. Our observations highlight the viability of sequence labeling techniques for sarcasm detection of dialogue. <s> BIB009 </s> Automatic Sarcasm Detection: A Survey <s> Role of Context in Sarcasm Detection <s> Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports a simple topic model for sarcasm detection, a first, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluation shows that topics such as `work', `gun laws', `weather' are sarcasm-prevalent topics. Our model is also able to discover the mixture of sentiment-bearing words that exist in a text of a given sentiment-related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25\%. <s> BIB010
An emerging trend in sarcasm detection is the use of context. The term context here refers to any information beyond the text to be predicted, and beyond common knowledge. For example, the sentence "I love solving math problems all weekend" may not be sarcastic to a student who loves math, but may be sarcastic to many others. This example requires context outside of the text to be classified. In the rest of this section, we describe approaches that work with examples like these. We refer to the textual unit to be classified as the "target text." As we will see, this context may be incorporated in a variety of ways-in general, using either supplementary data or supplementary information from the source platform providing the data. Reference BIB001 is the first annotation study that highlighted the need for context for sarcasm detection. The annotators mark Reddit comments with sarcasm labels. During this annotation, annotators often request for additional context in the form of Reddit comments in the thread. The authors also present a transition matrix that shows how many times authors change their labels after this conversational context is displayed to them. Following this observation and the promise of context for sarcasm detection, several recent approaches have looked at ways of incorporating it. The contexts that have been reported are of three types: (1) Author-specific context: This type of context refers to the textual footprint of the author of the target text. For example, the statement "Nicki Minaj...I love her" may be an exaggeration (hyperbolic form of sarcasm). To understand the sarcasm therein, information specific to the author who wrote the text is useful. Reference BIB002 follows the intuition that a tweet is sarcastic either because it has words of contrasting sentiment in it or because there is sentiment that contrasts with the author's historical sentiment. Historical tweets by the same author are considered as the context. Named entity phrases in the target tweet are searched for in the timeline of the author to gather the true sentiment of the author. This historical sentiment is then used to predict whether the author is likely to be sarcastic, given the sentiment expressed towards the entity in the target tweet. Reference BIB003 incorporates context about the author using the author's past tweets. This context is captured as features for a classifier. The features deal with various dimensions. They use features about the author's familiarity with Twitter (in terms of use of hashtags), familiarity with language (in terms of words and structures), and familiarity with sarcasm. Reference Bamman and Smith BIB004 considers author context in features such as historical salient terms, historical topic, profile information, historical sentiment (how likely is he/she to be negative), and so on. Reference BIB008 captures author-specific embeddings using a neural network-based architecture. The author-specific embeddings are extracted based on 1,000 past tweets posted by the author. (2) Conversational context: This type of context refers to text in the conversation of which the target text is a part. Consider a simple exclamation, "Yeah right, I can see that!" This may or may not be sarcastic. To understand the sarcasm (if any), one needs to look at the conversation that the sentence is a part of. If the sentence preceding the exclamation is "I don't feel bad about my low grades at all," then the sarcasm in the exclamation can be inferred. Reference BIB004 captures such conversational context using pair-wise Brown features between the target tweet and the previous tweet in the conversation. In addition, they also use "audience" features. These are features of the tweet author who responded to the target tweet. Reference BIB005 shows that concatenation of the previous post in a discussion forum thread along with the target post leads to an improvement in precision. Reference BIB006 looks at comments in the thread structure to obtain context for sarcasm detection. To do so, they use the subreddit name and noun phrases from the thread to which the target post belongs. Reference BIB007 uses sequence labeling to capture conversational context. For a sequence of tweets in a conversation, they estimate the most probable sequence of three labels: happy, sad, and sarcastic, for the last tweet in the sequence. A similar approach is used in Reference BIB009 to predict sarcasm in every text unit in a sequence of utterances in a scene. (3) Topical context: This context follows the intuition that some topics are likely to evoke sarcasm more commonly than others. For example, a tweet about a controversial topic in a political discourse is more likely to evoke sarcasm than a tweet about weather. Reference BIB010 presents a sarcasm topic model that uses sentiment mixture in tweets to discover sarcasm-prevalent topics. They note how topics like gun laws are more likely to evoke sarcasm as compared to funeral or fathers' day. Reference BIB007 use topical context. To predict sarcasm in a tweet, they download tweets containing a hashtag in the tweet. Then, based on timestamps, they create a sequence of these tweets and use sequence labeling to detect sarcasm in the target tweet (the last in the sequence).