An Italian lexicon-based sentiment analysis approach for medical applications

Abstract

Sentiment analysis aims at extracting opinions and or emotions mainly from written text. The most popular problem in sentiment analysis certainly is polarity detection, which falls into the broader class of Natural Language Processing (NLP) problems of text classification. To date, state-of-the-art approaches to text classification use neural language models built on popular architectures such as Transformers. However, these approaches are difficult to apply in low-resource languages and domains, as for instance the Italian language or small clinical trials. Motivated by this, this paper presents VADER-IT, a lexicon-based algorithm for polarity prediction in written text, that is an adaptation to the Italian language of the popular VADER. Unlike VADER, our system also predicts a polarity class (i.e. positive, negative or neutral). The system was tested on a dataset of 5495 healthcare related reviews from QSalute https://www.qsalute.it/, reaching a micro averaged F1--score = 81% and a micro averaged Jaccard - score = 73%.