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3 septembre 2021
- doi: 10.4000/books.aaccademia.8225
- eisbn: 9791280136336
https://www.openedition.org/12554 , info:eu-repo/semantics/openAccess
Mots-clés
Computational Linguistics Fine-grained sentiment analysis Distributional Semantics Quantitative Linguistic Investigations Gender Bias Depression from Social Media Online Hate Speech Automatic Sarcasm Detection TrAVaSI AriEmozione AEREST COVID-19 Linguistic Ostracism in Social Networks Multilingual NLU E3C Project DistilBERT Twitter during Pandemic LAN000000 Linguistics CBXSujets proches
Reviews Reviews Reviews Theater reviews Reviews Television plays--Reviews Television reviews Reviews Newspapers--Sections, columns, etc.--Book section Health care delivery Health services Personal health services Medical services Medical and health care industry Delivery of medical care Health care Healthcare Delivery of health care Book reviews Reviews Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
Luca Bacco et al., « A Machine Learning approach for Sentiment Analysis for Italian Reviews in Healthcare », Accademia University Press, ID : 10.4000/books.aaccademia.8225
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Résumé
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 over-sampled 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 an F1-score of 91.21%.