If you search around the internet looking for applying Naive Bayes classification on text, you’ll find a ton of articles that talk about the intuition behind the algorithm, maybe some slides from a lecture about the math and some notation behind it, and a bunch of articles I’m not going to link here that pretty much just paste some code and call it an explanation.
So I’m going to try to do a little more here, by hopefully writing and explaining enough, is let you yourself write a working Naive Bayes classifier.
There are three sections here. First is setup, and what format I’m expecting your text to be in for the classification. Second, I’ll talk about how to run naive Bayes on your own, using slow Python data structures. Finally, we’ll use Python’s NLTK and it’s classifier so you can see how to use that, since, let’s be honest, it’s gonna be quicker. Note that you wouldn’t want to use either of these in production, so look for a follow up post about how you might go about doing that.
As always, twitter, and check out the full code on github.
Data from this is going to be from this UCSD Amazon review data set. I swear one of the biggest issues with running these algorithms on your own is finding a data set big and varied enough to get interesting results. Otherwise you’ll spend most of your time scraping and cleaning data that by the time you get to the ML part of the project, you’re sufficiently annoyed. So big thanks that this data already exists.
You’ll notice that this set has millions of reviews for products across 24 different classes. In order to keep the complexity down here (this is a tutorial post after all), I’m sticking with two classes, and ones that are somewhat far enough different from each other to show that classification works, we’ll be classifying baby reviews against tools and home improvement reviews.
First thing I want to do now, after unpacking the .gz file, is to get a train and test set that’s smaller than the 160,792 and 134,476 of baby and tool reviews respectively. For purposes here, I’m going to use 1000 of each, with 800 used for training, and 200 used for testing. The algorithms are able to support any number of training and test reviews, but for demonstration purposes, we’re making that number lower.
Check the github repo if you want to see the code, but I wrote a script that just takes the full file, picks 1000 random numbers, segments 800 into the training set, and 200 into the test set, and saves them to files with the names “train_CLASSNAME.json” and “test_CLASSNAME.json” where classname is either “baby” or “tool”.
Also, the files from that dataset are really nice, in that they’re already python objects. So to get them into a script, all you have to do is run “eval” on each line of the file if you want the dict object.
There really wasn’t a good place to talk about this, so I’ll mention it here before getting into either of the self, and nltk running of the algorithm. The features we’re going to use are simply the lowercased version of all the words in the review. This means, in order to get a list of these words from the block of text, we remove punctuation, lowercase every word, split on spaces, and then remove words that are in the NLTK corpus of stopwords (basically boring words that don’t have any information about class).
from nltk.corpus import stopwords STOP_WORDS = set(stopwords.words('english')) STOP_WORDS.add('') def clean_review(review): exclude = set(string.punctuation) review = ''.join(ch for ch in review if ch not in exclude) split_sentence = review.lower().split(" ") clean = [word for word in split_sentence if word not in STOP_WORDS] return clean
Realize here that there are tons of different ways to do this, and ways to get more sophisticated that hopefully can get you better results! Things like stemming, which takes words down to their root word (wikipedia gives the example of “stems”, “stemmer”, “stemming”, “stemmed” as based on “stem”). You might want to include n-grams, for an n larger than 1 in our case as well.
Basically, there’s tons of processing on the text that you could do here. But since this I’m just talking about how Naive Bayes works, I’m sticking with simplicity. Maybe in the future I can get fancy and see how well I can do in classifying these reviews.
Ok, on to the actual algorithm.