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Published

2024-08-15

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Vol. 9 No. 7 (2024): Published

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Research Articles

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How to Cite

Xiang, yang, & Bin Zainal Abidin, A. H. (2024). Comparison of novel character relationship network mining and drama character relationship shaping algorithms. Environment and Social Psychology, 9(7). https://doi.org/10.59429/esp.v9i7.2076

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

yang Xiang

College of Arts and Sciences, Universiti Utara Malaysia, UUM Sintok Kedah Darul Aman

Ahmad Hisham Bin Zainal Abidin

College of Arts and Sciences, Universiti Utara Malaysia, UUM Sintok Kedah Darul Aman


DOI: https://doi.org/10.59429/esp.v9i7.2076


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

Abstract

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.

References

[1]. Andreescu, R., & Dimitriu, A. (Eds.). (2021). Handbook of Research on Contemporary Storytelling Methods Across New Media and Disciplines. IGI Global.

[2]. Gailea, N. (2023). Studies in drama. Cv. Azka pustaka.

[3]. Liu, S., Nie, W., Wang, C., Lu, J., Qiao, Z., Liu, L., ... & Anandkumar, A. (2023). Multi-modal molecule structure–text model for text-based retrieval and editing. Nature Machine Intelligence, 5(12), 1447-1457.

[4]. Li, J., & Engelbrecht, A. (2024). Advances in Sentiment Analysis: Techniques, Applications, and Challenges.

[5]. Jugran, s., kumar, a., tyagi, b. S., & Anand, V. (2021, March). Extractive automatic text summarization using SpaCy in Python & NLP. In 2021 International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE) (pp. 582-585). IEEE.

[6]. Kolluru, K., Adlakha, V., Aggarwal, S., & Chakrabarti, S. (2020). Openie6: Iterative grid labelling and coordination analysis for open information extraction. arXiv preprint arXiv:2010.03147.

[7]. Wajahat, A., Nazir, A., Akhtar, F., Qureshi, S., Razaque, F., & Shakeel, A. (2020, January). Interactively visualize and analyze the social network Gephi. In 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1- IEEE.

[8]. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need: Advances in neural information processing systems 2017; 30.

[9]. Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, 2014; 1532–1543.

[10]. L Akritidis, P Bozanis, “Low-Dimensional Text Representations for Sentiment Analysis NLP Tasks”, SN Computer Science 4 (5), 474.Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 2013.

[11]. Kusner M, Sun Y, Kolkin N, Weinberger K. From word embeddings to document distances. In: Proceedings of the 32nd international conference on machine learning, 2015; 957–966.

[12]. M. Nijila and M. T. Kala, "Extraction of Relationship Between Characters in Narrative Summaries," 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR), Ernakulam, India, 2018, pp. 1-5, doi: 10.1109/ICETIETR.2018.8529125.

[13]. A. I. Rathnayake, I. Kumari Ganegala, I. S. Samarasinghe, S. Bandara Weerasekara, A. I. Gamage and T. Thilakarathna, "Adjusting The Hard Level on Game by a Prediction for Improving Attraction and Business Value," 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, 2020, pp. 310-311, doi: 10.1109/ICTer51097.2020.9325474.

[14]. Xiaoxuan Chen and C. Qi, "A super-resolution method for recognition of license plate character using LBP and RBF," 2011 IEEE International Workshop on Machine Learning for Signal Processing, Beijing, China, 2011, pp. 1-5, doi: 10.1109/MLSP.2011.6064550.

[15]. M. Park, S. Park and H. Shin, "Literature Representation using Character Networks based on Sentiment Analysis," 2022 IEEE International Conference on Big Data and Smart Computing (BigComp), Daegu, Korea, Republic of, 2022, pp. 190-193, doi: 10.1109/BigComp54360.2022.00044.



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