Negative emotions in literature: a computational approach to tone and mood

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I have developed emotion lexicons for emotion association and emotion intensity. These lexicons were used to create affective word embeddings from an initial corpus of 1000 works of literature. In an iterative process, the lexicons can then be enhanced and expanded and used to fine-tune the emotion detection model for the specific domain and to adjust for semantic shifts in language. We have been able to show that mood can quite accurately be detected computationally by focusing on the first three paragraphs of a book. A by-product of this project was the development of a Finnish chapterize package that splits these books into chapters automatically.The findings were disseminated in two peer-reviewed papers and presented at several international conferences. Three more papers are currently in peer-review. Both the lexicons and the literature corpus have been made publicly available.