Fig. 2
From: Identifying tweets of personal health experience through word embedding and LSTM neural network
The pipeline to represent study tweets and classify the tweets. A total of 12,331 annotated tweets for training and test were preprocessed first. The index of each term in the preprocessed tweets was retrieved from the vocabulary, and the text of each tweet was converted to a sequence of the vectors of the corresponding term indices (see Fig. 3). Sequences of term index vectors were fed to the LSTM network for classification