EmotionAlBERTo: Emotion Recognition of Italian Social Media Texts Through BERT

Social networks are perceived by users as a natural environment for publicly sharing their thoughts and emotions about different subjects. In these platforms, despite the availability of various forms of communication, text is the most widespread way of communication. Therefore, the development of automatic techniques for emotion recognition in social media texts, such as tweets or Facebook posts, gives the opportunity of extracting information that could be valuable for many application fields, ranging from the analysis of customer satisfaction to the optimization of political campaigns. Nowadays, several emotion classifiers and datasets have been built for English texts while few resources are available for other languages. For this reason, in this work we present a deep learning algorithm for emotion recognition in Italian social media texts. The algorithm was named EmotionAlBERTo as it is based on AlBERTo, a BERT-based language understanding model for the Italian language. We trained and evaluated EmotionALBERTo on two different Twitter datasets: the MultiEmotions-It dataset and TwIT, a novel Italian dataset for emotion recognition that we built by collecting and manually labelling a corpus of about 3100 Italian tweets. Experiments show that the models achieve remarkable performance on both 4- class and 6-class emotion classification, by respectively obtaining F1= 0.91 and F1= 0.83 on MultiEmotions-It, F1= 0.92 and F1= 0.86 on TwIT.