通过三阶段分析，自动拆解电影剧本的情感弧与叙事结构。
Three Stage Narrative Analysis; Plot-Sentiment Breakdown, Structure Learning and Concept Detection
- 分三阶段解析：情感弧提取、结构学习、概念识别
- 基于自定义词典与聚类，准确捕捉剧情情感变化模式
- 适合影视内容分析、读者选剧或故事创作参考
故事理解与分析是自然语言理解中的长期挑战。自动化叙事分析需深层语义表征与句法处理，且面对海量叙事数据，必须依赖自动化语义分析与计算学习。本文提出一种框架，用于分析电影剧本的情感弧，并拓展至角色背景相关分析。该框架可提取叙事中蕴含的高阶与低阶概念。采用基于词典的情感分析方法，使用由StoryLab模块构建的自定义词典，其依据NRC-VAD数据集的效价、唤醒度和支配度评分。进一步通过Ward层次聚类技术对相似情感轨迹进行聚类。在电影数据集上的实验表明，该分析结果有助于观众与读者选择叙事内容。
原文摘要 · Abstract (English)
Story understanding and analysis have long been challenging areas within Natural Language Understanding. Automated narrative analysis requires deep computational semantic representations along with syntactic processing. Moreover, the large volume of narrative data demands automated semantic analysis and computational learning rather than manual analytical approaches. In this paper, we propose a framework that analyzes the sentiment arcs of movie scripts and performs extended analysis related to the context of the characters involved. The framework enables the extraction of high-level and low-level concepts conveyed through the narrative. Using dictionary-based sentiment analysis, our approach applies a custom lexicon built with the LabMTsimple storylab module. The custom lexicon is based on the Valence, Arousal, and Dominance scores from the NRC-VAD dataset. Furthermore, the framework advances the analysis by clustering similar sentiment plots using Wards hierarchical clustering technique. Experimental evaluation on a movie dataset shows that the resulting analysis is helpful to consumers and readers when selecting a narrative or story.
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