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I have a ton of short stories about 500 words long and I want to categorize them into one of, let's say 20 categories:

• Entertainment
• Food
• Music
• etc

I can hand-classify a bunch of them, but I want to implement machine learning to guess the categories eventually. What's the best way to approach this? Is there a standard approach to machine learning I should be using? I don't think a decision tree would work well since it's text data...I'm completely new in this field.

Any help would be appreciated, thanks!

## 1 Answer

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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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