Irony Detection: from the Twittersphere to the News Space

Automatic detection of irony is one of the hot topics for sentiment analysis, as it changes the polarity of text. Most of the work has been focused on the detection of figurative language in Twitter data due to relative ease of obtaining annotated data, thanks to the use of hashtags to signal irony. However, irony is present generally in natural language conversations and in particular in online public fora. In this paper, we present a comparative evaluation of irony detection from Italian news fora and Twitter posts. Since irony is not a very frequent phenomenon, its automatic detection suffers from data imbalance and feature sparseness problems. We experiment with different representations of text – bag-of-words, writing style, and word embeddings to address the feature sparseness; and balancing techniques to address the data imbalance