Identifying health related occupations of twitter users through word embedding and deep neural networks

HIGHLIGHTS

• who: Kazi Zainab from the Department of Computer, Lakehead University, Canada have published the article: Identifying health related occupations of Twitter users through word embedding and deep neural networks, in the Journal: (JOURNAL)
• what: The authors propose method of combining word embedding with state-of-art neural network models that include: Long Short Term Memory (LSTM) Bidirectional LSTM Gated Recurrent Unit Bidirectional Encoder Representations from Transformers lite BERT. The authors have presented novel method of detecting the occupations of users engaged in the medical domain by merging word embedding use sharing adaptation distribution reproduction in . . .
  

   

  

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