I have used a linear SVM model to perform a text classification. As I computed the TF-IDF features, which are based on the frequencies of term occurrence in documents, I'm worried about the effect of text size on the results.
My training corpus contained midsize text, and the model performs well. Now I would like to use it on short documents in which frequencies are probably less relevant. As a result, the model could be less reliable.
Is there a minimum text size under which it doesn't make sense to use the same model (this size depending on my training corpus)?
Or do I miss something and the TF-IDF inherently prevents such misbehaviour from the model?