Affect as a proxy for literary mood
This addresses the challenge of distinguishing tone from mood in literary analysis for researchers, though it appears incremental in enhancing existing emotion lexicons.
The paper tackled the problem of computationally detecting mood in literary texts by using affect as a proxy, resulting in a method that produces real-world congruent results closely matching qualitative analyses.
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.