  With a growing interest in modeling inherent subjectivity in natural
language, we present a linguistically-motivated process to understand and
analyze the writing style of individuals from three perspectives: lexical,
syntactic, and semantic. We discuss the stylistically expressive elements
within each of these levels and use existing methods to quantify the linguistic
intuitions related to some of these elements. We show that such a multi-level
analysis is useful for developing a well-knit understanding of style - which is
independent of the natural language task at hand, and also demonstrate its
value in solving three downstream tasks: authors' style analysis, authorship
attribution, and emotion prediction. We conduct experiments on a variety of
datasets, comprising texts from social networking sites, user reviews, legal
documents, literary books, and newswire. The results on the aforementioned
tasks and datasets illustrate that such a multi-level understanding of style,
which has been largely ignored in recent works, models style-related
subjectivity in text and can be leveraged to improve performance on multiple
downstream tasks both qualitatively and quantitatively.
