Year: 2018
Type: conference-paper
Abstract: This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to distinguish between irony and non-irony in English tweets.We create a targeted feature set and analyse how different features are useful in the task of irony detection, achieving an F 1 -score of 0.5914.The analysis of individual features provides insight that may be useful in future attempts at detecting irony in tweets.
DOI: 10.18653/v1/s18-1096
Authors: Edward Dearden, Alistair Baron
Institution: Lancaster University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2807071089