Original paper

Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation

Volume: 25, Issue: 1, Pages: 72 - 80
Published: May 13, 2017
Abstract
Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to...
Paper Details
Title
Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation
Published Date
May 13, 2017
Volume
25
Issue
1
Pages
72 - 80
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