In this paper, we present our approach to the task of binary sentiment classification for Italian reviews in healthcare domain. We first collected a new dataset for such domain. Then, we compared the results obtained by two different systems, one including a Support Vector Machine and one with BERT. For the first one, we linguistic pre-processed the dataset to extract hand-crafted features exploited by the classifier. For the second one, we oversampled the dataset to achieve better results. Our results show that the SVM-based system, without the worry of having to oversample, has better performance than the BERT-based one, achieving anF1-score of 91.21%.
A Machine Learning approach for Sentiment Analysis for Italian Reviews in Healthcare
Cimino A;Dell'Orletta F
2020
Abstract
In this paper, we present our approach to the task of binary sentiment classification for Italian reviews in healthcare domain. We first collected a new dataset for such domain. Then, we compared the results obtained by two different systems, one including a Support Vector Machine and one with BERT. For the first one, we linguistic pre-processed the dataset to extract hand-crafted features exploited by the classifier. For the second one, we oversampled the dataset to achieve better results. Our results show that the SVM-based system, without the worry of having to oversample, has better performance than the BERT-based one, achieving anF1-score of 91.21%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
