Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes
Keywords:Sentiment analysis, Naïve Bayes, Money Heist, Youtube, Opinion Mining
Netflix has produced many TV series, one of which is Money Heist. This series has four seasons with a total of 38 episodes. The fourth season was released on April 3, 2020, which has eight episodes. The fourth season of Money Heist is 31.73 times more demand than the average series around the world. However, despite the many requests for the fourth season of the Money Heist series, there are still some negative comments made by the connoisseurs of the Money Heist series. In the YouTube comments column on the Netflix channel, there are still many who comment neutral and provide positive comments on this series. Therefore, there needs to be a method in which viewers' comments or opinions can be analyzed in order to be able to classify the opinions they make about this series by conducting sentiment analysis using the Naïve Bayes algorithm. Based on the results of research conducted, Naive Bayes can be said to be successful in conducting sentiment analysis because it achieves results of 81%, 74.83%, and 75.22% for accuracy, precision, and recall, respectively.
X. Wu et al., "Top 10 algorithms in data mining," Knowl. Inf. Syst., vol. 14, no. 1, pp. 1-37, Jan. 2008. https://doi.org/10.1007/s10115-007-0114-2
L. C. Huang, S. Y. Hsu, and E. Lin, "A comparison of classification methods for predicting chronic fatigue syndrome based on genetic data," J. Transl. Med., vol. 7, p. 81, 2009. https://doi.org/10.1186/1479-5876-7-81
M. Wibowo, S. Sulaiman, and S. M. Shamsuddin, "Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore," Int. J. Innov. Comput., vol. 8, no. 3, pp. 65-71, 2018. https://doi.org/10.11113/ijic.v8n3.202
E. Sutoyo and A. Almaarif, "Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm," J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 1, pp. 95-101, 2020. https://doi.org/10.29207/resti.v4i1.1502
A. Aninditya, M. A. Hasibuan, and E. Sutoyo, "Text Mining Approach Using TF-IDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Bloom's Taxonomy," in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 2019, pp. 112-117. https://doi.org/10.1109/IoTaIS47347.2019.8980428
E. Sutoyo and A. Almaarif, "Twitter Sentiment Analysis of The Relocation of Indonesia's Capital City," Bull. Electr. Eng. Informatics, vol. 9, no. 04, pp. 1620-1630, 2020. http://doi.org/10.11591/eei.v9i4.2352
N. Altrabsheh, M. M. Gaber, and M. Cocea, "SA-E: Sentiment analysis for education," Front. Artif. Intell. Appl., vol. 255, pp. 353-362, 2013.
P. Bhargavi and S. Jyothi, "Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils," IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 9, no. 8, pp. 117-122, 2009.
N. Indurkhya and F. J. Damerau, Handbook of natural language processing, second edition. 2010. https://doi.org/10.1201/9781420085938
I. Rish, "An empirical study of the naive Bayes classifier," in IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, vol. 3, no. 22, pp. 41-46.
T. Nasukawa and J. Yi, "Sentiment analysis: Capturing favorability using natural language processing," in Proceedings of the 2nd International Conference on Knowledge Capture, K-CAP 2003, 2003. https://doi.org/10.1145/945645.945658
I. P. Cvijikj and F. Michahelles, "Understanding social media marketing: A case study on topics, categories and sentiment on a Facebook brand page," in Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek 2011, 2011. https://doi.org/10.1145/2181037.2181066
C. Troussas, M. Virvou, K. J. Espinosa, K. Llaguno, and J. Caro, "Sentiment analysis of Facebook statuses using Naive Bayes Classifier for language learning," in IISA 2013 - 4th International Conference on Information, Intelligence, Systems and Applications, 2013. https://doi.org/10.1109/IISA.2013.6623713
M. W. Berry and J. Kogan, Text Mining: Applications and Theory. 2010. https://doi.org/10.1002/9780470689646
L. Zhang, R. Ghosh, M. Dekhil, M. Hsu, and B. Liu, "Combining lexicon-based and learning-based methods for twitter sentiment analysis," HP Lab. Tech. Rep., 2011.
M. Hall, "A decision tree-based attribute weighting filter for Naive Bayes," in Research and Development in Intelligent Systems XXIII - Proceedings of AI 2006, the 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, 2007. https://doi.org/10.1007/978-1-84628-663-6_5
S. Taheri and M. Mammadov, "Learning the naive bayes classifier with optimization models," Int. J. Appl. Math. Comput. Sci., 2013. https://doi.org/10.2478/amcs-2013-0059
How to Cite
Copyright (c) 2020 Risky Novendri, Annisa Syafarani Callista, Danny Naufal Pratama, Chika Enggar Puspita
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.