Question

I’m trying to take a long list of objects (in this case, applications from the iTunes App Store) and classify them more specifically. For instance, there are a bunch of applications currently classified as “Education,” but I’d like to label them as Biology, English, Math, etc.

Is this an AI/Machine Learning problem? I have no background in that area whatsoever but would like some resources or ideas on where to start for this sort of thing.

Answer 1

Yes, you are correct. Classification is a machine learning problem, and classifying stuff based on text data involves natural language processing.

The canonical classification problem is spam detection using a Naive Bayes classifier, which is very simple. The idea is as follows:

1. Gather a bunch of data (emails), and label them by class (spam, or not spam)
2. For each email, remove stopwords, and get a list of the unique words in that email
3. Now, for each word, calculate the probability it appears in a spam email, vs a non-spam email (ie count occurrences in spam, vs non spam)
4. Now you have a model- the probability of a email being spam, given it contains a word. However, an email contains many words. In Naive Bayes, you assume the words occur independently of each other (which turns out to to be an ok assumption), and multiply the probabilities of all words in the email against each other.
5. You usually divide data into training and testing, so you’ll have a set of emails you train your model on, and then a set of labeled stuff you test against where you calculate precision and recall.

I’d highly recommend playing around with NLTK, a python machine learning and nlp library. It’s very user friendly and has good docs and tutorials, and is a good way to get acquainted with the field.

EDIT: Here’s an explanation of how to build a simple NB classifier with code.