Topic Similarity Measure | Multi-class Text Classification Model
LDA is amazing for topic modelling, but naming the topics (regardless of what algorithm you use) is inevitably a disappointing process, because the labels you’re thinking of are very subjective and the algorithm has little respect for what words we want to go in each topic :p

The way I would approach your problem is:


Train an LDA topic model, using grid search rather than intuition to select the right number of topics
Transform your training examples into k-dimensional vectors using your trained topic model
Add your training vectors to a k-d tree and classify new tweets using a k-nearest neighbours approach