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Comparison of novel character relationship network mining and drama character relationship shaping algorithms

yang Xiang, Ahmad Hisham Bin Zainal Abidin

Article ID: 2511
Vol 9, Issue 7, 2024, Article identifier:

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This study embarks on an interdisciplinary journey to analyze and compare Character Relationships (CR) in two diverse storytelling mediums – the classic novel “Emma” by Jane Austen and the popular TV series “Friends”. Leveraging a blend of Natural Language Processing (NLP) and advanced video analysis tools, this research “sheds light on” the intricate network of CR within these narratives. This study scientifically analyzes these relationships' complete method, creation, and impact using sentiment analysis, object identification, and story coherence algorithms. Qualitative and quantitative metrics such as precision, recall, F-score, and accuracy assist in explaining character updates. The content and TV exhibit distinct storytelling modes, and this study demonstrates that algorithmic analysis of stories is practical. The findings of this study request to contribute to online studies by focusing on understanding character networks and their vital role in TV storytelling.


novel character relationship; NLP; machine learning; sentiment analysis; network mining; precision; recall; F-score; accuracy

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