Using a naive Bayes will most probably work for you. The method is like:
You should fix a number of categories and simply train data set of (document, category) pairs.
A data vector from your document will be sth like a bag of words. e.g. Take the 100 most common words except words like "the", "and" and such. Each word should get a fixed component of your data vector. A feature vector is an array of booleans, each indicating whether the word came up in the corresponding document.
Training:
For your training set, calculate the probability of every feature and every class:
p(C) = number documents of class C / total number of documents
Calculate the probability of a feature in a class: p(F|C) = number of documents of class with given feature (= word "food" is in the text) / number of documents in the given class.
Decision:
Given an unclassified document, the probability of it belonging to class C is proportional to
P(C|F1, ..., F500) = P(C) * P(F1|C) * P(F2|C)
P(F500|C)
Since multiplication is numerically difficult, you can use the sum of the logs instead, which will maximize at the same
C: log P(C|F1, ..., F500) = log P(C) + log P(F1|C) + log P(F2|C) + ... + log P(F500|C)
Hope this answer helps.
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