  We propose to use affect as a proxy for mood in literary texts. In this
study, we explore the differences in computationally detecting tone versus
detecting mood. Methodologically we utilize affective word embeddings to look
at the affective distribution in different text segments. We also present a
simple yet efficient and effective method of enhancing emotion lexicons to take
both semantic shift and the domain of the text into account producing
real-world congruent results closely matching both contemporary and modern
qualitative analyses.
