Litcius/Paper detail

C-Net: Contextual Network for Sarcasm Detection

Amit Kumar Jena, Aman Sinha, Rohit Agarwal

202031 citationsDOIOpen Access PDF

Abstract

Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0% F1-score on the Twitter dataset and 66.3% F1-score on Reddit dataset.

Topics & Concepts

SarcasmUtteranceSentenceComputer scienceTask (project management)Artificial intelligenceContext (archaeology)Natural language processingMachine learningIronySpeech recognitionLinguisticsBiologyPaleontologyPhilosophyEconomicsManagementSentiment Analysis and Opinion MiningTopic ModelingNatural Language Processing Techniques