I am working on a text classification problem on tweets. At the moment I was only considering the content of the tweets as a source of information, and I was using a simple bag of words approach using term frequencies as features, using Random Forests (this is something I cannot change).
Now my idea is to try to incorporate information present in the URLs used in tweets. Now, not all the tweets have URLs, and if I decide to use the same term frequency representation also for URLs I will have a huge number of features only from URLs. For this reason, I suppose that having a single set of features containing both the tweet term frequencies and the URL term frequencies could be bad. Besides I'll have to fill some impossible values (like -1) for the URL features for tweets that do not have URLs, and I will probably worsen the classification for this tweets, as I will have a huge number of uninformative features.
Do you have any suggestions regarding this issue?