An ever-increasing share of information is recorded as digital text. Until recently, however, text analysis relied on meticulous human examination, a method inherently limited in scalability when confronted with the vast corpora now at our disposal. The quantity of individual documents within widely-used databases, such as those containing newspaper articles and tweets from Twitter, has surged into the tens of millions or billions more. This burgeoning abundance of large-scale corpora has sparked a heightened enthusiasm for machine learning techniques for text analysis, a trajectory that is poised to persist and intensify with the continual expansion of textual data reservoirs. This workshop will introduce how to perform text analysis using machine learning tools. The main topics covered include: What Is Text Analysis? Text data preprocessing. An overview of text analysis techniques used in economics. Word (text) similarity analysis with an example of measuring aggregate-level Brexit uncertainty. Topic analysis with an example of measuring topic-level Brexit uncertainty. Sentiment analysis with an example of measuring public sentiment towards Brexit using Twitter data.