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2022 | OriginalPaper | Chapter

Convolutional and Recurrent Neural Networks for Opinion Mining on Drug Reviews

Authors : Nesma Settouti, Fatiha Youbi

Published in: Deep Learning for Social Media Data Analytics

Publisher: Springer International Publishing

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Abstract

Online health forums are places where patients share their experiences about their disease(s), treatment(s), etc. Under the cover of anonymity, they express their personal experiences very freely. These forums are therefore a very useful source of information for healthcare professionals to better identify and understand the problems, behaviors, and sentiments of their patients. In this study, our goal is to unveil the secrets of sentiment analysis on drug reviews using deep learning-based approaches. More specifically, we focus our research on genericity, to reduce human intervention in the excavation process. We, therefore, explore the problem of opinion mining using methods based on deep convolutional and recurrent neuronal networks like CNN, LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and hybrid models to reach complementarity, instead of standard methods which require a priori resources such as vocabulary, and sentence structure. In this work, we attempt to extend the genericity of our process by reducing this need for resources a priori.

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Title: Convolutional and Recurrent Neural Networks for Opinion Mining on Drug Reviews
Authors: Nesma Settouti
Fatiha Youbi
Publisher: Springer International Publishing
Book: Deep Learning for Social Media Data Analytics
Print ISBN: 978-3-031-10868-6

Electronic ISBN: 978-3-031-10869-3

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-10869-3_4

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