FEEL-IT: Emotion and Sentiment Classification for the Italian Language
Federico Bianchi, Debora Nozza, Dirk Hovy
Abstract
While sentiment analysis is a popular task to understand people’s reactions online, we often need more nuanced information: is the post negative because the user is angry or sad? An abundance of approaches have been introduced for tackling these tasks, also for Italian, but they all treat only one of the tasks. We introduce FEEL-IT, a novel benchmark corpus of Italian Twitter posts annotated with four basic emotions: anger, fear, joy, sadness. By collapsing them, we can also do sentiment analysis. We evaluate our corpus on benchmark datasets for both emotion and sentiment classification, obtaining competitive results. We release an open-source Python library, so researchers can use a model trained on FEEL-IT for inferring both sentiments and emotions from Italian text.