🤖 AI Summary
This study addresses the challenge of automated semantic analysis and multi-level narrative concept extraction from large-scale narrative texts, such as film screenplays. We propose a three-stage computational framework: (1) sentiment arc modeling using a VAD-semantic–customized lexicon integrating LabMTsimple and NRC-VAD; (2) plot structure learning via Ward hierarchical clustering; and (3) context-aware, joint detection of high- and low-level narrative concepts grounded in character semantics. The method balances interpretability with computational efficiency, significantly improving accuracy in sentiment trajectory identification and consistency in plot-pattern clustering. Experiments on multiple screenplay datasets demonstrate the framework’s effectiveness in supporting narrative quality assessment and personalized content recommendation. It provides a scalable, reproducible technical pathway for computational narratology.
📝 Abstract
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.