Irony Detection

Irony detection in natural language processing aims to automatically identify ironic expressions, a challenging task due to the nuanced and context-dependent nature of irony. Current research focuses on leveraging deep learning models, particularly transformer architectures like BERT, T5, and GPT-2, often enhanced by incorporating emotion analysis and refined feature selection techniques such as TF-IDF. These advancements improve the accuracy of irony detection, particularly in social media contexts, with a focus on understanding the interplay between lexical features, sentiment, and topic modeling. This work has implications for various applications, including sentiment analysis, social media monitoring, and improving human-computer interaction.