Comparing deep learning architectures for sentiment analysis on drug reviews

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dc.contributor.author Colon Ruiz, Cristobal
dc.contributor.author Segura-Bedmar, Isabel
dc.date.accessioned 2020-11-11T11:31:10Z
dc.date.issued 2020-10
dc.identifier.bibliographicCitation Colón-Ruiz, C., Segura-Bedmar, I. (2020). Comparing deep learning architectures for sentiment analysis on drug reviews. Journal of Biomedical Informatics, 110
dc.identifier.issn 1532-0464
dc.identifier.uri http://hdl.handle.net/10016/31367
dc.description.abstract Since 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.
dc.description.sponsorship This 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).
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2020 Elsevier Inc. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Sentiment analysis
dc.subject.other Multi-class text classification
dc.subject.other Deep learning
dc.subject.other Convolutional neural network
dc.subject.other Long short-term memory
dc.subject.other Bidirectional encoder representations from transformers
dc.title Comparing deep learning architectures for sentiment analysis on drug reviews
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1016/j.jbi.2020.103539
dc.rights.accessRights embargoedAccess
dc.relation.projectID Gobierno de España. TIN2017-87548-C2-1-R
dc.relation.projectID Comunidad de Madrid. NLP4Rare-CM-UC3M
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 33
dc.identifier.publicationtitle JOURNAL OF BIOMEDICAL INFORMATICS
dc.identifier.publicationvolume 110
dc.identifier.uxxi AR/0000026067
carlosiii.embargo.liftdate 2021-10
carlosiii.embargo.terms 2021-10
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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