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...
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: | |
Online Access: | http://link.springer.com/article/10.1186/s12859-018-2198-y |
Similar Items
-
COVID-19 Tweets Classification Based on a Hybrid Word Embedding Method
by: Yosra Didi, et al.
Published: (2022-05-01) -
Emotion Classification of Indonesian Tweets using BERT Embedding
by: Muhammad Habib Algifari, et al.
Published: (2023-11-01) -
Dataset of Arabic spam and ham tweets
by: Sanaa Kaddoura, et al.
Published: (2024-02-01) -
Detection of Islamophobic Tweets on Twitter Using Sentiment Analysis
by: Buğra AYAN, et al.
Published: (2019-06-01) -
Evaluating Author Attribution on Emirati Tweets
by: Mahmoud Khonji, et al.
Published: (2020-01-01)