Identifying tweets of personal health experience through word embedding and LSTM neural network

Identifying tweets of personal health experience through word embedding and LSTM neural network

Abstract Background As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human d...

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Bibliographic Details
Main Authors: Keyuan Jiang, Shichao Feng, Qunhao Song, Ricardo A. Calix, Matrika Gupta, Gordon R. Bernard
Format: Article
Language:English
Published: BMC 2018-06-01
Series:BMC Bioinformatics
Subjects:
Health surveillance
Pharmacovigilance
Social media
Twitter
Deep learning
Unsupervised feature learning
Online Access:http://link.springer.com/article/10.1186/s12859-018-2198-y

Internet

http://link.springer.com/article/10.1186/s12859-018-2198-y

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