Analisi Sintattica per il Sentiment Analysis di Recensioni in ambito Sanitario
Authors: Giuseppe Attardi, Daniele Sartiano, Alessandro Lenci (Università di Pisa, 2021)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
English. In this paper, we describe our approach to the sentiment classification challenge on Italian reviews in the healthcare domain. Firstly, we followed the work of Bacco et al. Bacco2020AML from which we obtained the dataset. Then, we generated our model called based on <cit.>. Through an extensive comparative analysis of the results obtained, we showed how the use of syntax can improve performance in terms of both accuracy and F1-score compared to previously proposed models. Finally, we explored the interpretative power of -viz to explain the inferences made by neural networks on examples.

Introduction
People are practically reviewing anything in on-line sites and understanding the polarization of a comment through automatic sentiment classifier is a tantalizing challenge. In recent years, the number of virtual reviewers has drastically increased and there are many products and services, which can be reviewed.      Each person, before buying a product or a service, searches into reviews from people who have already had experienced the product or the service.  Review portals are usually linked to the leisure or business activities such as the world of tourism, e-commerce or movies.  However, there are topics where these reviews and the associated automatic computed sentiment may induce to select wrong services, which may dramatically affect personal life.   When dealing with health-related services, the effect of positive or negative reviews on hospitals and doctors can have a potential catastrophic impact on the health of who is using this piece of information. QSalute [https://www.qsalute.it/] is one of the most important Italian portals of reviews about hospitals, nursing homes and doctors. It is very important for patients to seek the best hospital for their condition based on the past experience of other patients. Reviews in the world of health benefit both patients and hospitals because they are a means to discover problems and solve them <cit.>.   Automatic sentiment analyzer have then a big responsibility in the context of health-related services.  In these sensitive areas, it is important to design AI systems whose decisions are transparent <cit.>, that is, the systems must give the motivation for the choice made so that people can trust. If the users do not trust a model or a prediction, they will not use it <cit.>.     In this article, we investigate a model that can mitigate the responsibility of sentiment analyzers for health-related services. The model we are using exploits syntactic information within neural networks to provide a clear visualisation of the internal decision mechanism of the model that produced the decision. We propose ( for HealthCare) based on <cit.> to solve the sentiment analysis task introduced by Bacco el al.Bacco2020AML. We use on QSalute Italian portal reviews in order to include symbolic knowledge as a part of the architecture and visualize the internal decision-making mechanism of the neural model, using -viz <cit.>.   In the rest of paper, Section <ref> gives details about the dataset and methods, while Section <ref> and <ref> describe the experiments, the results obtained and their discussion. Finally, in Section <ref> we present the final conclusions and future goals.

Conclusion
In this article, we investigated a model that can mitigate the responsibility of sentiment analyzers for health-related services. Our model exploits syntactic information within neural networks to provide a clear visualisation of its internal decision mechanism. is based on <cit.> and we worked in a sentiment analysis task introduced by Bacco el al.Bacco2020AML. We studied several versions of pre-trained BERT models on the Italian language and found out that AlBERTo is, among them, the best model for this task. However, , which is composed of +AlBERTo, outperforms better than AlBERTo model alone. Additionally, via -viz, we visualized the reasons why classifies the dataset. We observed how captures relevant syntactic information by catching the keywords in each sentence giving them more weight in the decision phase, mitigating and capturing possible errors of the sentiment analysers. Our future goal is to be able to have full control of the sentiment analysers by injecting human rules <cit.> in order to mitigate possible errors.