Correlating Historical Events and Cinematic Releases Using Web Information
Resumo
Mimesis is a term created by Aristotle and Plato in which art imitates life. Mimesis has been studied since ancient Greece and governed the theatrical and sculptural creations of the time. In this context, our work aims to study the effect of mimesis in the current cinematographic scenario, correlating historical events of the 20th and 21st centuries to the great cinematographic productions that follow. The question that guides our work is “Is there an increase in the release of films with a certain theme after a historical event?”. To answer this, we propose a methodology that uses two distinct data sources: one related to descriptions of historical facts from the 20th and 21st centuries extracted from Wikipedia and another with descriptions of films extracted from TMDb. Using topic modeling strategies, we automatically find the main themes related to historical events, and later, we evaluate how the description of a film is associated with the themes found. Temporal analysis is done to assess the popularity of each of the themes over time. In the results obtained by our methodology, there was a significant increase in the popularity of films that addressed themes related to historical events that occurred in an immediately preceding moment in time, corroborating the concept of mimesis.
Palavras-chave:
Topic Modeling, Movie Evolution, Mimesis
Referências
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Bradley Carron-Arthur, Julia Reynolds, Kylie Bennett, Anthony Bennett, and Kathleen M Griffiths. 2016. What’s all the talk about? Topic modelling in a mental health Internet support group. BMC psychiatry 16, 1 (2016), 367.
Saurav Ghosh, Prithwish Chakraborty, Elaine O Nsoesie, Emily Cohn, Sumiko R Mekaru, John S Brownstein, and Naren Ramakrishnan. 2017. Temporal topic modeling to assess associations between news trends and infectious disease outbreaks. Scientific reports 7(2017), 40841.
Derek Greene and James P. Cross. 2016. Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach. arxiv:1607.03055 [cs.CL]
C. Huang, Qing Wang, Donghui Yang, and Feifei Xu. 2018. Topic mining of tourist attractions based on a seasonal context aware LDA model. Intell. Data Anal. 22(2018), 383–405.
Carina Jacobi, Wouter van Atteveldt, and Kasper Welbers. 2016. Quantitative analysis of large amounts of journalistic texts using topic modelling. Digital Journalism 4, 1 (2016), 89–106. https://doi.org/10.1080/21670811.2015.1093271
Daniel D. Lee and H. Sebastian Seung. 2000. Algorithms for Non-Negative Matrix Factorization. In Proceedings of the 13th International Conference on Neural Information Processing Systems (Denver, CO) (NIPS’00). MIT Press, Cambridge, MA, USA, 535–541.
Washington Luiz, Felipe Viegas, Rafael Alencar, Fernando Mourão, Thiago Salles, Dárlinton Carvalho, Marcos Andre Gonçalves, and Leonardo Rocha. 2018. A Feature-Oriented Sentiment Rating for Mobile App Reviews. In Proceedings of the 2018 World Wide Web Conference. 1909–1918.
Jian Ming Luo, Huy Quan Vu, Gang Li, and Rob Law. 2020. Topic modelling for theme park online reviews: analysis of Disneyland. Journal of Travel & Tourism Marketing 37, 2 (2020), 272–285. https://doi.org/10.1080/10548408.2020.1740138
Gunther Martin. 2018. Euripides, ”Ion”: Edition and Commentary. De Gruyter. https://books.google.com.br/books?id=95pQDwAAQBAJ
Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2018. Advances in Pre-Training Distributed Word Representations. In LREC’18.
Sergey I. Nikolenko, Sergei Koltcov, and Olessia Koltsova. 2017. Topic Modelling for Qualitative Studies. J. Inf. Sci. 43, 1 (Feb. 2017), 88–102. https://doi.org/10.1177/0165551515617393
Antonio Pedro, Antônio Pereira, Pablo Cecilio, Nayara Pena, Felipe Viegas, Elisa Tuler, Diego Dias, and Leonardo Rocha. 2021. An Article-Oriented Framework for Automatic Semantic Analysis of COVID-19 Researches. In Computational Science and Its Applications - ICCSA 2021 - 21st International Conference, Cagliari, Italy, September 13-16, 2021, Proceedings, Part III(Lecture Notes in Computer Science, Vol. 12951). Springer, 172–187. https://doi.org/10.1007/978-3-030-86970-0_13
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global Vectors for Word Representation.. In EMNLP.
Shahzad Qaiser and Ramsha Ali. 2018. Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications 181, 1(2018), 25–29.
Alper Kursat Uysal and Serkan Gunal. 2014. The impact of preprocessing on text classification. Information Processing & Management 50, 1 (2014), 04 – 112. https://doi.org/10.1016/j.ipm.2013.08.006
Allard J. van Altena, P. Moerland, A. Zwinderman, and S. Olabarriaga. 2016. Understanding big data themes from scientific biomedical literature through topic modeling. Journal of Big Data 3(2016), 1–21.
Felipe Viegas, Sérgio Canuto, Christian Gomes, Washington Luiz, Thierson Rosa, Sabir Ribas, Leonardo Rocha, and Marcos André Gonçalves. 2019. CluWords: exploiting semantic word clustering representation for enhanced topic modeling. In Proceedings of the Twelfth ACM WSDM. 753.
Felipe Viegas, Washington Cunha, Christian Gomes, Antônio Pereira, Leonardo Rocha, and Marcos Goncalves. 2020. CluHTM - Semantic Hierarchical Topic Modeling based on CluWords. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 8138–8150. https://doi.org/10.18653/v1/2020.acl-main.724
Felipe Viegas, Washington Luiz, Christian Gomes, Amir Khatibi, Sérgio Canuto, Fernando Mourão, Thiago Salles, Leonardo Rocha, and Marcos André Gonçalves. 2018. Semantically-Enhanced Topic Modeling. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 893–902. https://doi.org/10.1145/3269206.3271797
S Vijayarani, Ms J Ilamathi, and Ms Nithya. 2015. Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks 5, 1, 7–16.
Publicado
07/11/2022
Como Citar
SANTOS, Brenno Lemos Melquiades; ALBERGARIA, Elisa Tuler De; DIAS, Diego Roberto Colombo; PIGOZZO, Alexandre Bittencourt; ROCHA, Leonardo Chaves Dutra Da.
Correlating Historical Events and Cinematic Releases Using Web Information. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 28. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 189-192.
