Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features
Abstract
Social media has become a very popular source of information. With this popularity comes an interest in systems that can classify the information produced. This study tries to create such a system detecting irony in Twitter users. Recent work emphasize the importance of lexical features, sentiment features and the contrast herein along with TF-IDF and topic models. Based on a thorough feature selection process, the resulting model contains specific sub-features from these areas. Our model reaches an F1-score of 0.84, which is above the baseline. We find that lexical features, especially TF-IDF, contribute the most to our models while sentiment and topic modeling features contribute less to overall performance. Lastly, we highlight multiple interesting and important paths for further exploration.
Cite
@article{arxiv.2311.04885,
title = {Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features},
author = {Tibor L. R. Krols and Marie Mortensen and Ninell Oldenburg},
journal= {arXiv preprint arXiv:2311.04885},
year = {2023}
}