  Text classifiers are at the core of many NLP applications and use a variety
of algorithmic approaches and software. This paper introduces infrastructure
and methodologies for text classifiers based on large-scale regular
expressions. In particular, we describe how Facebook determines if a given
piece of text - anything from a hashtag to a post - belongs to a narrow topic
such as COVID-19. To fully define a topic and evaluate classifier performance
we employ human-guided iterations of keyword discovery, but do not require
labeled data. For COVID-19, we build two sets of regular expressions: (1) for
66 languages, with 99% precision and recall >50%, (2) for the 11 most common
languages, with precision >90% and recall >90%. Regular expressions enable
low-latency queries from multiple platforms. Response to challenges like
COVID-19 is fast and so are revisions. Comparisons to a DNN classifier show
explainable results, higher precision and recall, and less overfitting. Our
learnings can be applied to other narrow-topic classifiers.
