  Framing is a process of emphasizing a certain aspect of an issue over the
others, nudging readers or listeners towards different positions on the issue
even without making a biased argument. {Here, we propose FrameAxis, a method
for characterizing documents by identifying the most relevant semantic axes
("microframes") that are overrepresented in the text using word embedding. Our
unsupervised approach can be readily applied to large datasets because it does
not require manual annotations. It can also provide nuanced insights by
considering a rich set of semantic axes. FrameAxis is designed to
quantitatively tease out two important dimensions of how microframes are used
in the text. \textit{Microframe bias} captures how biased the text is on a
certain microframe, and \textit{microframe intensity} shows how actively a
certain microframe is used. Together, they offer a detailed characterization of
the text. We demonstrate that microframes with the highest bias and intensity
well align with sentiment, topic, and partisan spectrum by applying FrameAxis
to multiple datasets from restaurant reviews to political news.} The existing
domain knowledge can be incorporated into FrameAxis {by using custom
microframes and by using FrameAxis as an iterative exploratory analysis
instrument.} Additionally, we propose methods for explaining the results of
FrameAxis at the level of individual words and documents. Our method may
accelerate scalable and sophisticated computational analyses of framing across
disciplines.
