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Comparing deep learning architectures for sentiment analysis on drug reviews

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorColón Ruiz, Cristóbal
dc.contributor.authorSegura-Bedmar, Isabel
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-11-11T11:31:10Z
dc.date.available2021-10-01T23:00:05Z
dc.date.issued2020-10
dc.description.abstractSince the turn of the century, as millions of user’s opinions are available on the web, sentiment analysis has become one of the most fruitful research fields in Natural Language Processing (NLP). Research on sentiment analysis has covered a wide range of domains such as economy, polity, and medicine, among others. In the pharmaceutical field, automatic analysis of online user reviews allows for the analysis of large amounts of user’s opinions and to obtain relevant information about the effectiveness and side effects of drugs, which could be used to improve pharmacovigilance systems. Throughout the years, approaches for sentiment analysis have progressed from simple rules to advanced machine learning techniques such as deep learning, which has become an emerging technology in many NLP tasks. Sentiment analysis is not oblivious to this success, and several systems based on deep learning have recently demonstrated their superiority over former methods, achieving state-of-the-art results on standard sentiment analysis datasets. However, prior work shows that very few attempts have been made to apply deep learning to sentiment analysis of drug reviews. We present a benchmark comparison of various deep learning architectures such as Convolutional Neural Networks (CNN) and Long short-term memory (LSTM) recurrent neural networks. We propose several combinations of these models and also study the effect of different pre-trained word embedding models. As transformers have revolutionized the NLP field achieving state-of-art results for many NLP tasks, we also explore Bidirectional Encoder Representations from Transformers (BERT) with a Bi-LSTM for the sentiment analysis of drug reviews. Our experiments show that the usage of BERT obtains the best results, but with a very high training time. On the other hand, CNN achieves acceptable results while requiring less training time.en
dc.description.sponsorshipThis work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R) and the Interdisciplinary Projects Program for Young Researchers at Universidad Carlos III of Madrid, Spain founded by the Community of Madrid (NLP4Rare-CM-UC3M).en
dc.identifier.bibliographicCitationColón-Ruiz, C., Segura-Bedmar, I. (2020). Comparing deep learning architectures for sentiment analysis on drug reviews. Journal of Biomedical Informatics, 110en
dc.identifier.doihttps://doi.org/10.1016/j.jbi.2020.103539
dc.identifier.issn1532-0464
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage33
dc.identifier.publicationtitleJOURNAL OF BIOMEDICAL INFORMATICSen
dc.identifier.publicationvolume110
dc.identifier.urihttps://hdl.handle.net/10016/31367
dc.identifier.uxxiAR/0000026067
dc.language.isoenges
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TIN2017-87548-C2-1-Res
dc.relation.projectIDComunidad de Madrid. NLP4Rare-CM-UC3Mes
dc.rights© 2020 Elsevier Inc. All rights reserved.es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherSentiment analysisen
dc.subject.otherMulti-class text classificationen
dc.subject.otherDeep learningen
dc.subject.otherConvolutional neural networken
dc.subject.otherLong short-term memoryen
dc.subject.otherBidirectional encoder representations from transformersen
dc.titleComparing deep learning architectures for sentiment analysis on drug reviewsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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