Noise in online networks especially knowledge networks such as Quora, Yahoo! Q&A, reddit can be attributed to jokes, redundancy, insults, sarcasm. As the size of the content on these websites grows in a manner not possible to be monitored manually, there is a need to automatically detect the undesired text to increase the signal (useful content) to noise ratio. Popular machine learning algorith...

In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers. Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization, dialog systems and sentiment analysis is well recognized. Sarcasm can often ...

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...

This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification. We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic e...

Previous research [9] suggests that Dutch learners of (British) English are not able to express sarcasm prosodically in their L2. The present study investigates whether explicit training on the prosodic markers of sarcasm in English can improve learners’ realisation of sarcasm. Sarcastic speech was elicited in short simulated telephone conversations between Dutch advanced learners of English an...

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 pos...

The ability to reliably identify sarcasm and irony in text can improve the performance of many Natural Language Processing (NLP) systems including summarization, sentiment analysis, etc. The existing sarcasm detection systems have focused on identifying sarcasm on a sentence level or for a specific phrase. However, often it is impossible to identify a sentence containing sarcasm without knowing...

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 ...

BACKGROUND Interpersonal communication problems are common among persons with schizophrenia and may be linked, in part, to deficits in theory of mind (ToM), the ability to accurately perceive the attitudes, beliefs and intentions of others. Particular difficulties might be expected in the processing of counterfactual information such as sarcasm or lies. METHOD The present study included 50 sc...

Sarcasm annotation extends beyond linguistic expertise, and often involves cultural context. This paper presents our first-of-its-kind study that deals with impact of cultural differences on the quality of sarcasm annotation. For this study, we consider the case of American text and Indian annotators. For two sarcasmlabeled datasets of American tweets and discussion forum posts that have been a...