TF-IDF Features vs Embedding Layer

Question

Have you guys tried to compare the performance of TF-IDF features* with a shallow neural network classifier vs a deep neural network models like an RNN that has an embedding layer with word embedding as weights next to the input layer? I tried this on a couple of tweet datasets and got surprising results: f1 score of~65% for the TF-IDF vs ~45% for the RNN. I tried the setup embedding layer + shallow fully connected layer vs TF-IDF + fully connected layer but got almost same result difference. Can you guys give some opinion on how TF-IDF features can outperform the embedding layer of a deep NN? Is this case common? Thanks!

• I've used unigrams and bigrams to produce the TF-IDF features