Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical
  Features
Tibor L. R. Krols Marie Mortensen Ninell Oldenburg
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.