For an informative overview geared toward econometricians, see the new paper, "Text as Data" by Matthew Gentzkow, Bryan T. Kelly, and Matt Taddy (GKT). (Ungated version here.)
"Text as data" has wide applications in economics. As GKT note:
... in finance, text from financial news, social media, and company filings is used to predict asset price movements and study the causal impact of new information. In macroeconomics, text is used to forecast variation in inflation and unemployment, and estimate the effects of policy uncertainty. In media economics, text from news and social media is used to study the drivers and effects of political slant. In industrial organization and marketing, text from advertisements and product reviews is used to study the drivers of consumer decision making. In political economy, text from politicians’ speeches is used to study the dynamics of political agendas and debate.
There are three key steps:
1. Represent the raw text D as a numerical array x
2. Map x into predicted values yhat of outcomes y
3. Use yhat in subsequent descriptive or causal analysis.
GKT emphasize the ultra-high dimensionality inherent in statistical text analyses, with connections to machine learning, etc.