Question

I’m fairly new at machine learning and text mining in general. It has come to my attention the presence of a ruby library called Liblinear https://github.com/tomz/liblinear-ruby-swig.

What I want to do so far is train the software to identify whether a text mentions anything related to bicycles or not.

Can someone please highlight the steps that I should be following (i.e: preprocessing text and how), share resources and ideally share a simple example to get me going.

Any help will do, thanks!

Answer 1

The classical approach is:

1. Collect a representative sample of input texts, each labeled as related/unrelated.
2. Divide the sample into training and test sets.
3. Extract all the terms in all the documents of the training set; call this the vocabulary, V.
4. For each document in the training set, convert it into a vector of booleans where the i‘th element is true/1 iff the i‘th term in the vocabulary occurs in the document.
5. Feed the vectorized training set to the learning algorithm.

Now, to classify a document, vectorize it as in step 4. and feed it to the classifier to get a related/unrelated label for it. Compare this with the actual label to see if it went right. You should be able to get at least some 80% accuracy with this simple method.

To improve this method, replace the booleans with term counts, normalized by document length, or, even better, tf-idf scores.