&gt; Is it common practice to use the most frequent words as features ? It looks like they don&#x27;t carry much information, by definition.<p>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 (<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tf%E2%80%93idf" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tf%E2%80%93idf</a>), which comes included in Keras.<p>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.