Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features
This work addresses the challenge of irony detection for social media analysis, but it is incremental as it builds on existing feature-based approaches.
The study tackled the problem of detecting irony in Twitter posts by developing a classification system using lexical, sentiment, and topic features, achieving an F1-score of 0.84 that outperformed the baseline.
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.