> Is it common practice to use the most frequent words as features ? It looks like they don't carry much information, by definition.
The common practice with a small-ish dataset is to use e.g. the top 10k or 20k most frequent words, but filter out the top 50-100 so most frequent words, as those indeed do not carry much information. A commonly used weighting scheme is TF-IDF (https://en.wikipedia.org/wiki/Tf%E2%80%93idf), which comes included in Keras.
Anyway, this is a cool ML starter project. Keras makes it really easy to do this sort of fast experimentation with a range of different neural networks models.