How to add incorporate meta data into text classification?
Some models cannot really handle this, while others lend themselves for it easily. I'll explain two approaches that you could use:

Naive bayes

With Naive Bayes you can use other categorical values as well as your normal n-grams or sparse bag of words vectors. Just add them one-hot encoded to your features and it is also incorporated. With numerical features you would need to use something like Gaussian Naive Bayes, to fit a distribution to your features per target class, then you can use the likelihoods of these features per class to compute the probabilities.

Neural network

If you use a neural network approach like CNNs or RNNs, you can add any type of feature representation network and concatenate it somewhere in your original network. In your case you would have a softmax at the end of your RNN. Before this, concatenate the output of your 'normal-feature' neural network, add some dense layers and feed this to your softmax output layer. This way you can train your model end-to-end and it will learn important interactions as well.