  Emotions are a central aspect of communication. Consequently, emotion
analysis (EA) is a rapidly growing field in natural language processing (NLP).
However, there is no consensus on scope, direction, or methods. In this paper,
we conduct a thorough review of 154 relevant NLP publications from the last
decade. Based on this review, we address four different questions: (1) How are
EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and
which emotions are modeled? (3) Is the subjectivity of emotions considered in
terms of demographics and cultural factors? and (4) What are the primary NLP
applications for EA? We take stock of trends in EA and tasks, emotion
frameworks used, existing datasets, methods, and applications. We then discuss
four lacunae: (1) the absence of demographic and cultural aspects does not
account for the variation in how emotions are perceived, but instead assumes
they are universally experienced in the same manner; (2) the poor fit of
emotion categories from the two main emotion theories to the task; (3) the lack
of standardized EA terminology hinders gap identification, comparison, and
future goals; and (4) the absence of interdisciplinary research isolates EA
from insights in other fields. Our work will enable more focused research into
EA and a more holistic approach to modeling emotions in NLP.
