  Music has been commonly recognized as a means of expressing emotions. In this
sense, an intense debate emerges from the need to verbalize musical emotions.
This concern seems highly relevant today, considering the exponential growth of
natural language processing using deep learning models where it is possible to
prompt semantic propositions to generate music automatically. This scoping
review aims to analyze and discuss the possibilities of music generation
conditioned by emotions. To address this topic, we propose a historical
perspective that encompasses the different disciplines and methods contributing
to this topic. In detail, we review two main paradigms adopted in automatic
music generation: rules-based and machine-learning models. Of note are the deep
learning architectures that aim to generate high-fidelity music from textual
descriptions. These models raise fundamental questions about the expressivity
of music, including whether emotions can be represented with words or expressed
through them. We conclude that overcoming the limitation and ambiguity of
language to express emotions through music, some of the use of deep learning
with natural language has the potential to impact the creative industries by
providing powerful tools to prompt and generate new musical works.
