It should be noted that CNNs and LSTMs are an order of magnitude slower than things like bag-of-words/fasttext unless you're using an expensive GPU, and the accuracy benefit if any may be marginal in practice. Kaggle prioritizes chasing a metric, but real-world data science has more considerations.
Do you know of a good way to combine fasttext with none text features?
Let's say I know seasonality is a strong feature in classifying my text, how can I add this? With a BOW I can literally just add SEASON_AUTUMN as a word to the text, and I'll get that extra feature as a dummy variable in my feature vector.
But for fasttext, if I add such a word, it will just be averaged out in the final document feature vector.