  Online platform businesses can be identified by using web-scraped texts. This
is a classification problem that combines elements of natural language
processing and rare event detection. Because online platforms are rare,
accurately identifying them with Machine Learning algorithms is challenging.
Here, we describe the development of a Machine Learning-based text
classification approach that reduces the number of false positives as much as
possible. It greatly reduces the bias in the estimates obtained by using
calibrated probabilities and ensembles.
