Machine learning text classification where a text belongs to 1 to N classes

Question

So I am trying to (just for fun) classify movies based on their description, the idea is to "tag" movies, so a given movie might be "action" and "humor" at the same time for example.

Normally when using a text classifier, what you get is the class to where a given text belongs, but in my case I want to assign a text to 1 to N tags.

Currently my training set would look like this

+--------------------------+---------+
|        TEXT              |  TAG    |
+--------------------------+---------+
| Some text from a movie   |  action |
+--------------------------+---------+
| Some text from a movie   |  humor  |
+--------------------------+---------+
| Another text here        | romance |
+--------------------------+---------+
| Another text here        | cartoons|
+--------------------------+---------+
| And some text more       | humor   |
+--------------------------+---------+

What I am doing next is to train classifiers to tell me whether or not each tag belongs to a single text, so for example, if I want to figure out whether or not a text is classified as "humor" I would end up with the following training set

+--------------------------+---------+
|        TEXT              |  TAG    |
+--------------------------+---------+
| Some text from a movie   |  humor  |
+--------------------------+---------+
| Another text here        |not humor|
+--------------------------+---------+
| And some text more       | humor   |
+--------------------------+---------+

Then I train a classifier that would learn whether or not a text is humor or not (the same approach is done with the rest of the tags). After that I end with a total of 4 classifiers that are

• action / no action
• humor / no humor
• romance / no romance
• cartoons / no cartoons

Finally when I get a new text, I apply it to each of the 4 classifiers, for each classifier that gives me a positive classification (that is, gives me X instead of no-X) if such classification is over a certain threshold (say 0.9), then I assume that the new text belongs to tag X, and then I repeat the same with each of the classifiers.

In particular I am using Naive Bayes as algorithm, but the same could be applied with any algorithm that outputs a probability.

Now the question is, is this approach correct? Am I doing something terribly wrong here? From the results I get things seems to make sense, but I would like a second opinion.

Answer 1

Yes, this makes sense. it is a well known, basic technique for multilabel/multiclass classification known as "one vs all" (or "one vs all") classifier. This is very old and widely used. On the other hand - it is also very naive as you do not consider any relations between your classes/tags. You might be interested in reading about structure learning, which covers topics where there is some structure over labels space that can be exploited (and usually there is).

Answer 2

The problem you've described can be addressed by Latent Dirichlet Allocation, a statistical topic model method to find the underlying ("latent") topics in a collection of documents. This approach is based on a model where each document is a mixture of these topics.

In general, you initially decide upon the topics (in your case, the tags are the topics) and then run a trainer. The LDA software will then output a probability distribution over the topics for each document.

Here is a good introduction: http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/

Answer 3

Yes. your approach is correct and it is a well-known strategy to enable classifiers that are designed to do binary classification to handle multi-class classification tasks.

Andrew Ng (of Standford University) explains this approach here. Even though it is explained for logistic regression, as you mentioned the idea can be applied to any algorithm that outputs probability.