How to do feature selection after using pre-trained word embeddings?
When using a RNN, you don't feed all the data at once, you usually have a seq2seq model.
The models are created with an encoder-decoder architecture. The LSTM is used in the encoding phase.

So, let's say you have a text of 78 words. You will feed the embedding vector (size 300) of those 78 words, 1-by-1 into your LSTM and in the end you will get a hidden vector which represents your sentence.

Then, you can take this hidden vector and use it for classification (with a feedforward neural network, for example).

So, it doesn't matter that you have 4293 unique words in your data. You need to feed your LSTM a sequence of size [&lt;number of words in sentence&gt; × 300].