I have a lot of twitter data (4GB) related to keyword Ebola. I want to classify the tweets into 21 categories.
- Death - tweet is about death
- Health Care Workers - tweet is about Health Care Workers.
- Hospitals and Treatment Facilities
Like these there are 21 categories.
If the categories were less like 4 or 5. I would have used Naive Bayes or SVM. But since tweet text is only 140 characters. I don't know if using Naive Bayes or any other supervised learning techniques is suitable here for so many categories.
I also don't think something like clustering or LDA can give good results.
I can't think of any other way to move forward as categories are not very dissimilar and some overlapping of meaning is there.
So, the question is how do I classify tweets with so many categories? Which method will be more applicable here?