Automatic Irony Detection for Romanian Online News

In this paper, we propose a new corpus and approach to evaluate and detect the irony in online news published in Romanian. Irony detection is based on subjective perceptions, thus the human experience plays an important role in this field. We propose a supervised machine learning system based on a Romanian ironic and non-ironic news corpus, manually annotated, and a Romanian dictionary of commonly used words. We present a comparison of the best results by applying several classification algorithms, such as Naïve Bayes, Logistic Regression, Linear SVC, Decision Trees, and Random Forest. We present aspects about irony detection and some language resources, considering a Romanian corpus based on 25,841 non-ironic news items and 14,064 ironic news items collected from online sources, which is used by the proposed irony detection system. Furthermore, we present the results of a sentiment analysis system based on a supervised machine learning approach applied to the 8,128 news items representing our evaluation corpus. The scope of the paper is to present the proposed system and a practical solution for automatic irony detection in Romanian real-world applications, which can provide useful information about online media perception. The best result of the compared systems, after having processed the corpus and applied the filtering method is over 91 %, achieved by the Naïve Bayes algorithm.