Algorithm for text classification
Take a look at term frequency and inverse document frequency also cosine similarity to find important words to create categories and assign documents to categories based on similarity
EDIT:
Found an example here
Interesting articles :
- A self-organizing semantic map for information retrieval
- WEBSOM - self-organizing maps of document collections
The major issue IMHO here is the length of the documents. I think I would call it phrase classification and there is work going on on this because of the twitter thing. You could bring in additional text performing a web search on the 30 words and then analyzing the top matches. There is a paper about this but I can't find it right now. Then I would try a feature vector approach (tdf-idf as in Jimmy's answer) and a multiclass SVM for classification.
Perhaps a decision tree combined with a NN?
You can use SVM Algorithm for Classify text in C# with libsvm.net library.
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