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.

翻译：我们提出在文学文本中，将情感作为基调的代理指标。本研究探索了在计算层面检测语气与检测基调之间的差异。在方法论上，我们利用情感词嵌入来分析不同文本段落中的情感分布。同时，我们提出一种简单但高效且有效的方法，用于增强情感词典，使其同时考虑语义变迁和文本领域，从而生成了与现实世界相符的结果，与当代和现代的定性分析高度吻合。