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

我已经开发了情感词汇的情感协会和情感强度。这些词汇被用来从1000部文学作品的初始语料库中创建情感词嵌入。在一个迭代过程中，词典可以被增强和扩展，并用于微调特定领域的情感检测模型，并调整语言中的语义变化。我们已经能够证明，通过关注一本书的前三段，可以相当准确地通过计算检测情绪。这一项目的一个副产品是开发了一个芬兰章节汇编包，将这些书籍自动分成章节，研究结果在两份同行审查的论文中分发，并在几次国际会议上介绍。另有三篇论文目前正在进行同行审查。词典和文献语料库都已公开提供。

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.