# What's the best way to include non-word features into text classification model?

I have a text classification task with multiple classes. Right now model uses logistic regression & uses only word features encoded using TF-IDF. But for some of the classes, the non-text features are very useful, such as, length of the text in words, etc.

My question is how it's better to include such features into model, together with text features - how to normalize values, because text length has completely different range of values, comparing to the encoded text features, etc.