Precise semantic representation of a sentence and definitive information extraction are key steps in the accurate processing of sentence meaning, especially for figurative phenomena such as sarcasm, Irony, and metaphor cause literal meanings to be discounted and secondary or extended meanings to be intentionally profiled. Semantic modelling faces a new challenge in social media, because grammatical inaccuracy is commonplace yet many previous state-of-the-art methods exploit grammatical structure. For sarcasm detection over social media content, researchers so far have counted on Bag-of-Words(BOW), N-grams etc. In this paper, we propose a neural network semantic model for the task of sarcasm detection. We also review semantic modelling using Support Vector Machine (SVM) that employs constituency parsetrees fed and labeled with syntactic and semantic information. The proposed neural network model composed of Convolution Neural Network(CNN) and followed by a Long short term memory (LSTM) network and finally a Deep neural network(DNN). The proposed model outperforms state-of-the-art textbased methods for sarcasm detection, yielding an F-score of .92.
This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.
In this paper, we provide an in-depth cognitive analysis of a specific humor strategy we coin “trumping”, a multi-agent language game that revolves around the subversion of the linguistic forms of exchange. In particular, we illustrate how, in a conversational setting, agents can “reflect” and “distort” the linguistic-conceptual construal of each others' utterances. Because this reflection or parallelism in the trumping game can be situated on different levels of linguistic organization, a multi-dimensional semantic-pragmatic account is proposed. Using insights from cognitive linguistics, we show that adversarial agents exploit the conceptual mechanisms underlying the opponent's utterances in order to turn the tables in the humor game. In doing so, an agent can trump an adversary by demonstrating a “hyper-understanding” of the lexico-conceptual meaning of an opponent's utterance. This subversion of construal operations like metaphor, metonymy and salience leads to a sudden manipulation of the discourse space that has been set up in the previous utterance(s) (Langacker 2001). In general, by providing an analysis in terms of basic principles of semantic construal, we argue that a cognitive linguistic treatment of humor has an ecological validity that is lacking in most linguistic humor research.
Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker's mindset. Insight into a speaker's psychological profile at the time of production is a valuable source of context for sarcasm detection. Using a neural architecture, we show significant gains in detection accuracy when knowledge of the speaker's mood at the time of production can be inferred. Our focus is on sarcasm detection on Twitter, and show that the mood exhibited by a speaker over tweets leading up to a new post is as useful a cue for sarcasm as the topical context of the post itself. The work opens the door to an empirical exploration not just of sarcasm in text but of the sarcastic state of mind.
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