Weka can use data from multiple formats, for example the simplest is csv input.
Now, first you would need some library for doing LDA, I would suggest you to try R for that there is a fantastic lda package which does all you need. There are also java packages, it really doesn't matter what you choose the process is pretty much the same
The main thing you need to do is pre-process the text. For example, you filter out short words (e.g., the, a, on, of) and for example convert everything to lowercase. You do that for all the documents in your corpus.
Then you need to create a specific object called Document-Term Matrix.
The rows in the matrix are individual documents, and the columns are the words.
The values in each cell are 1s or 0s indicating whether a word appears in a given document.
You make this matrix by first finding all unique words from all the documents and create a long list of them. Then, for each document you make a list of 1s and 0s which indicates whether a word at particular position appears in that document.
Then you either make a nested list (in Java it would be List>) or matrix (in Java it would be Integer) or some similar object depending on a particular library you're using.
The next thing you need to define is the number of topics you're interested in extracting. Probably you'll try different values to see which one looks the best for your problem. There are also some more formal ways of deciding the number of topics, but this is less important for you at the moment.
After this, you just invoke the LDA algorithm where you provide this document term matrix and the desired number of topics and the rest is magic :)
As a result, you'll be given for each document the most likely topic which you can use as a numeric feature in Weka as any other classification feature.