I'm interning in an auditing company and have audit reports that require categorising. I have been using simple text mining + classification techniques in R (DocumentTermMatrix in tm package, SVM via e1071 package, etc.) to help.

Unfortunately it is laborious to manually categorise the issues to create the train data, but as of now I have about 50+ samples categorised into about 7 categories. A shockingly small number, I know.

What then is the best way to continue? Is 50 too small a size for training data? I have about 150 reports that requires categorising. The reports are each roughly about 900 - 2000 chars (1-2 pages) long (hence laborious to manually categorize).

  • $\begingroup$ Honestly, I'd stick to categorizing them by-hand, or preparing some heuristic rules to categorize them (e.g. existence of some particular terms in documents etc). $\endgroup$
    – Tim
    Commented Jul 21, 2017 at 7:33
  • $\begingroup$ yes, i've thought about heuristics (e.g. certain terms coming up) but it is hard to come up with rigid rules because everyone writes differently and the presence of a term (e.g. "inaccurate") does not determine the report's category (e.g. "inaccurate report") $\endgroup$
    – hongsy
    Commented Jul 21, 2017 at 7:37

1 Answer 1


Did you try latent semantic analysis? Maybe you will be able to find meaningful patterns when you visualize the data in 2 or 3 dimensions.

If you have mostly unlabeled data then maybe you can leverage it using semi-supervised approach, for example label propagation.


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