Stories are rich in the emotions they exhibit in their narratives and evoke
in the readers. The emotional journeys of the various characters within a story
are central to their appeal. Computational analysis of the emotions of novels,
however, has rarely examined the variation in the emotional trajectories of the
different characters within them, instead considering the entire novel to
represent a single story arc. In this work, we use character dialogue to
distinguish between the emotion arcs of the narration and the various
characters. We analyze the emotion arcs of the various characters in a dataset
of English literary novels using the framework of Utterance Emotion Dynamics.
Our findings show that the narration and the dialogue largely express disparate
emotions through the course of a novel, and that the commonalities or
differences in the emotional arcs of stories are more accurately captured by
those associated with individual characters.

小说情感轨迹的计算分析很少考虑不同角色之间的情感变化，而我们的研究使用角色对话来区分叙述和不同角色的情感轨迹，并发现小说中的情感轨迹通过叙述和对话表达的不同情感更能准确地捕捉故事的共性或差异。

文学小说的情感动态

The Emotion Dynamics of Literary Novels

Automatically generated emotion arcs -- that capture how an individual or a
population feels over time -- are widely used in industry and research.
However, there is little work on evaluating the generated arcs in English
(where the emotion resources are available) and no work on generating or
evaluating emotion arcs for low-resource languages. Work on generating emotion
arcs in low-resource languages such as those indigenous to Africa, the
Americas, and Australia is stymied by the lack of emotion-labeled resources and
large language models for those languages. Work on evaluating emotion arcs (for
any language) is scarce because of the difficulty of establishing the true
(gold) emotion arc. Our work, for the first time, systematically and
quantitatively evaluates automatically generated emotion arcs. We also compare
two common ways of generating emotion arcs: Machine-Learning (ML) models and
Lexicon-Only (LexO) methods. By running experiments on 42 diverse datasets in 9
languages, we show that despite being markedly poor at instance level emotion
classification, LexO methods are highly accurate at generating emotion arcs
when aggregating information from hundreds of instances. (Predicted arcs have
correlations ranging from 0.94 to 0.99 with the gold arcs for various
emotions.) We also show that for languages with no emotion lexicons, automatic
translations of English emotion lexicons can be used to generate high-quality
emotion arcs -- correlations above 0.9 with the gold emotion arcs in all six
indigenous African languages explored. This opens up avenues for work on
emotions in numerous languages from around the world; crucial not only for
commerce, public policy, and health research in service of speakers of those
languages, but also to draw meaningful conclusions in emotion-pertinent
research using information from around the world (thereby avoiding a
western-centric bias in research).

本文通过定量和系统地评估，比较了两种情感环路的常见方法：机器学习模型和仅词典方法。为了在世界范围内从情感相关研究中得出有意义的结论（从而避免西方中心主义偏见），作者说明使用自动翻译的英语情感词典可以用于生成高质量的情感弧。