Unsupervised Irony Detection: A Probabilistic Model with Word Embeddings

Best Paper Award 🏅


The automatic detection of figurative language, such as irony and sarcasm, is one of the most challenging tasks of Natural Language Processing (NLP). This is because machine learning methods can be easily misled by the presence of words that have a strong polarity but are used ironically, which means that the opposite polarity was intended. In this paper, we propose an unsupervised framework for domain-independent irony detection. In particular, to derive an unsupervised Topic-Irony Model (TIM), we built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we took advantage of Word Embeddings to obtain domain-aware ironic orientation of words. This is the first work that addresses this task in unsupervised settings and the first study on the topic-irony distribution. Experimental results have shown that TIM is comparable, and sometimes even better with respect to supervised state of the art approaches for irony detection. Moreover, when integrating the probabilistic model with word embeddings (TIM+WE), promising results have been obtained in a more complex and real world scenario.