This is a walkthrough that will show you how easy it is to build a custom classifier and we will give you an example data set to use for this. 

In only a couple minutes, you'll be able to see how a machine learns to recognize text right before your eyes. 🤯

We are going to build a classifier that can tag hotel reviews by topic or aspect (Cleanliness, Comfort, Food, Location). 

The end result will be able to classify new reviews by those tags, in the same fashion as you see below. This example is tagging NPS feedback by topic. The results are shown to the right with the corresponding confidence level.

1) Download the file of hotel reviews by clicking below. It's a CSV file with the first column containing the review. Each review is on its own row.

2) Create a custom model in MonkeyLearn and choose "Classifier".

3) Upload the CSV file that you previously downloaded, and select the column that has the text we want (the first one).

4) Create the following 4 Tags:

5) Tag the texts that appear by selecting the appropriate tag (or tags) and clicking confirm (as shown below)

After tagging a couple reviews, you will see that the model learns to classify automatically and will start making suggestions for tags. It's machine learning at work. 

Once you are done tagging, you can test it to see how it works.

You can make your own custom classifier just as easily by following the same five steps (if you are on a free account, you can delete the one you just made in your model settings).

For more on this, see our guide on making custom classifiers.