The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Abdul Azzam Ajhari

Abstract


Online movie streaming platforms have changed the current pattern of watching movies. Besides providing convenience in watching anywhere and anytime, this service is provided at a lower cost to moviegoers. The increase in moviegoers on online streaming platforms has resulted in easy-to-find reviews. This review can determine whether they decide to watch the film or not. The moviegoers' reviews can be easily and quickly found for analysis using sentiment analysis to find a film's worthiness. This study used sentiment analysis in classifying Twitter data predictions using the Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM). In the sentiment analysis of labeling with positive and negative categories, a distilled version of BERT (DistilBERT) was used in this study. With a little human assistance in preprocessing, the model worked objectively with an overall accuracy performance on the confusion matrix of 64.50% for the Multinomial Naive Bayes model and 64.12% for the Support Vector Machine model. Performance evaluation was also carried out by calculating the cross-validation accuracy, which resulted in an accuracy of 72.38% for the MNB. Meanwhile, the SVM model obtained an accuracy of 70.19%.


Keywords


Sentiment analysis, movie reviews, multinomial naïve bayes, support vector machine, distilBERT

Full Text:

PDF

References


Y. Jia., “The Streaming Service Under Pandemic with the Example of Performance of Disney”. Advances in Social Science, Education and Humanities Research. Proceedings of the 2021 International Conference on Social Development and Media Communication (SDMC 2021), vol. 631, 2021. [Online]. Available: https://www.atlantis-press.com/proceedings/sdmc-21/125968448.

Statista, “Number of Disney Plus subscribers worldwide from 1st quarter to 1st quarter 2022” [Online]. Available: https://www.statista.com/statistics/1095372/disney-plus-number-of-subscribers-us/ (accessed February, 2022).

E. Kontopoulos, C. Berberidis, T. Dergiades and N. Bassiliades, “Ontology-based sentiment analysis of twitter posts,” Expert Systems with Applications, vol. 40, pp. 4065-4074, doi: 10.1016/j.eswa.2013.01.001.

K. Sigit, A. P. Dewi, G. Windu, Nurmalasari, T. Muhamad and N. Kadinar, “Comparison of Classification Methods on Sentiment Analysis of Political Figure Electability Based on Public Comments on Online News Media Sites,” IOP Conf. Ser.: Mater. Sci. Eng., 2019, doi: 10.1088/1757-899X/662/4/042003.

Syahriani, A. A. Yana and T. Santoso, “Sentiment analysis of facebook comments on indonesian presidential candidates using the naïve bayes method,” J. Phys.: Conf. Ser., 2020, doi: 10.1088/1742-6596/1641/1/012012.

S. W. Handani, D. I. S. Saputra, Hasirun, R. M. Arino and G. F. A. Ramdhan, “Sentiment Analysis for Go-Jek on Google Play Store,” J. Phys.: Conf. Ser., 2019, doi: 10.1088/1742-6596/1196/1/012032.

H. Wisnu, M. Afif and Y. Ruldevyani, “Sentiment analysis on customer satisfaction of digital payment in Indonesia: A comparative study using KNN and Naïve Bayes,” J. Phys.: Conf. Ser., 2020, doi: 10.1088/1742-6596/1444/1/012034.

F. Ratnawati and E. Winarko, “Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 12, no. 1, 2018, doi: 10.22146/ijccs.19237.

J. Zheng, L. Zheng and L. Yang, “Research and Analysis in Fine-grained Sentiment of Film Reviews Based on Deep Learning,” J. Phys.: Conf. Ser., 2019, doi: 10.1088/1742-6596/1237/2/022152.

R. Maulana, P. A. Rahayuningsih, W. Irmayani, D. Saputra and W. E. Jayanti, “Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain,” J. Phys.: Conf. Ser., 2020, doi: 10.1088/1742-6596/1641/1/012060.

U. U. Acikalin, B. Bardak and M. Kutlu, “Turkish Sentiment Analysis Using BERT,” 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, 2020, doi: 10.1109/SIU49456.2020.9302492.

U. D. Gandhi, P. M. Kumar, G. C. Babu and G. Karthick, “Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM),” Wireless Pers Commun, 2021, doi: 10.1007/s11277-021-08580-3.

K. Lu and J. Wu, “Sentiment analysis of film review texts based on sentiment dictionary and SVM,” Association for Computing Machinery, pp. 73-77, 2019, doi: 10.1145/3319921.3319966.

L. Shang, L. Sui, S. Wang and D. Zhang, “Sentiment analysis of film reviews based on CNN-BLSTM-Attention,” J. Phys.: Conf. Ser., 2020, doi: 10.1088/1742-6596/1550/3/032056.

P. Tiwari, B. K. Mishra, S. Kumar and V. Kumar, “Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis,” Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, pp. 689-701, 2020, doi: 10.4018/978-1-7998-2460-2.ch036.

G. Xu, Y. Meng, X. Qiu, Z. Yu and X. Wu, “Sentiment analysis of comment texts based on BiLSTM,” IEEE Access, vol. 7, pp. 51522-51532, 2019, doi: 10.1109/ACCESS.2019.2909919.

R. Novendri, A. S. Callista, D. N. Pratama and C. E. Puspita, “Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes,” Bulletin of Computer Science and Electrical Engineering, vol. 1, no. 1, pp. 26-32, 2020, doi: 10.25008/bcsee.v1i1.5.

W. W. Thant and E. E. Mon. (2019). “Analyzing Sentiment of Myanmar Movie Comments Using Naïve Bayes Classifier”. In Proceedings of the Myanmar Universities’ Research Conference (MURC 2019). 2019. [Online]. Available: https://www.uit.edu.mm/storage/2020/08/16.-Analyzing-Sentiment-of-Myanmar-Movie-Comments-Using-Naive-Bayes-Classifier.pdf.

Y. Kim, M. Kang and S. R. Jeong, “Text mining and sentiment analysis for predicting box office success,” KSII Transactions on Internet and Information Systems, vol. 12, pp. 4090-4102, 2018, doi: 10.3837/tiis.2018.08.030.

A. S. Rathore, S. Arjaria, S. Khandelwal, S. Thorat and V. Kulkarni, “Movie rating system using sentiment analysis,” Advances in Intelligent Systems and Computing, vol. 742, pp. 85-98, 2018, doi: 10.1007/978-981-13-0589-4_9.

M. Hall, “A Decision Tree-Based Attribute Weighting Filter for Naive Bayes,” International Conference on Innovative Techniques and Applications of Artificial Intelligence. Research and Development in Intelligent Systems XXIII, SGAI 2016., pp. 59-70, doi: 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., vol. 23, no. 4, pp. 787-795, 2013, doi: 10.2478/amcs-2013-0059.

I. Rish., “An Empricial Study of the Naïve Bayes Classifier”, 2001. [Online], Available: https://www.researchgate.net/publication/228845263_An_Empirical_Study_of_the_Naive_Bayes_Classifier (accessed Mar, 2022).

V. Jakkula., “Tutorial on support vector machine”. School of EECS, Washington State University, [Online], 2006, Available: https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf.

E. Sutoyo and A. Almaarif, “Twitter sentiment analysis of the relocation of Indonesia’s capital city,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1620-1630, 2020, doi: 10.11591/eei.v9i4.2352.

V. Sanh, L. Debut, J. Chaumond and T. Wolf., “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter”. arXiv, 2020. [Online]. Available: https://arxiv.org/abs/1910.01108 (accessed March, 2022).

K. B. Soni, M. Chopade and R. Vaghela, “Credit Card Fraud Detection Using Machine Learning Approach,” Appl. Inf. Syst. Manag., vol. 4, no. 2, pp. 71-76, 2021, doi: 10.15408/aism.v4i2.20570.




DOI: https://doi.org/10.15408/aism.v6i1.26045 Abstract - 0 PDF - 0

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

EDITORIAL ADDRESS:

Department of Information Systems, Faculty of Science and Technology,
Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
Faculty of Science and Technology Building, 3rd Floor, 1st Campus, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No. 95, Ciputat Timur, Kota Tangerang Selatan, Banten 15412, Indonesia.
Tlp/Fax: +622174019 25/+62217493315.
E-mail: aism.journal@apps.uinjkt.ac.id, Website: https://journal.uinjkt.ac.id/index.php/aism

Creative Commons Licence
Applied Information System and Management (AISM) by the Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Indonesia is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://journal.uinjkt.ac.id/index.php/aism.