How to add more features in addition to a 100D word vector
You can add a second Input layer to your architecture.

Look at this link Keras Guide Multiple Inputs for a more detailed explanation.

The output of the embedding layer can be combined with the secondary input and passed to the next layer.

auxiliary_input = Input(shape=(5,), name='aux_input')
x = keras.layers.concatenate([lstm_out, auxiliary_input])