  Tradition tweet classification models for crisis response focus on
convolutional layers and domain-specific word embeddings. In this paper, we
study the application of different neural networks with general-purpose and
domain-specific word embeddings to investigate their ability to improve the
performance of tweet classification models. We evaluate four tweet
classification models on CrisisNLP dataset and obtain comparable results which
indicates that general-purpose word embedding such as GloVe can be used instead
of domain-specific word embedding especially with Bi-LSTM where results
reported the highest performance of 62.04% F1 score.
