You're trying to classify the sentences' sources, right? I would suggest grouping your training data by their sources (but only up to some fixed size, in which case you would have a duplicate entry for that source). E.g., if you are trying to classify the source of a tweet, you would make each tweet (not sentence, and that means each vector would represent a string of up to 140 characters) a vector and calculate its corresponding features, which should span the contents that the feature vector would relate to. Further if you could get each of the fixed bodies of text into ARFF format, where the last value in the vector would represent the source of the fixed body of text and the feature vectors would each have the same number of features (the features of which you would pick, more on this in a bit) then you wouldn't be too far from being able to use this suite of classifiers. As for which classifier you should use, I suggest skimming through this paper, produced by the same machine learning group that produced that suite of classifiers. 964
It should give you some pretty good insight.
For your choice of what features you would like to vectorize the fixed bodies into, it would be good to count classes of tags. For example, you could count the number of adverbs as a feature, and/or count the number of first-person/second-person/third-person pronouns as a feature, and/or count the number of verbs in a certain tense as a feature, etc. The features you choose are more so based on your understanding of the bodies. If your fixed bodies could include multiple sentences then some other features could also be the average length of sentences (with respect to number of tokens), the average length of tokens (omitting punctuation, for example, from this group), and/or even just the number of sentences in the fixed body.
Make sure to pick enough features, and make sure to pick features you think would be better at differentiating your classes of sources. For example, if any two of your sources are likely to differ in the formality of their tones, then a useful feature would be the number of slang tokens per fixed body. 312