Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database
- Samsir Samsir Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
- Kusmanto Kusmanto Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
- Abdul Hakim Dalimunthe Universitas Al Washliyah Labuhanbatu, Rantauprapat, Indonesia
- Rahmad Aditiya Teknik Informatika, Universitas Al-Washliyah Labuhanbatu, Indonesia
-
Ronal Watrianthos
*
Universitas Al Washliyah Labuhanbatu, Rantauprapat,
Indonesia
(*) Corresponding Author
Abstract
A film review is a subjective opinion of someone who has different feelings about each film. As a result, film enthusiasts will struggle to assess whether the film meets their requirements. Based on these issues, sentiment analysis is the best way to fix them. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text contains positive or negative thoughts. The Nave Bayes method was chosen because it can classify data based on the computation of each class's probability against objects in a given data sample. The best model was created utilizing data without lemmatization, 500 vector sizes, and Nave Bayes classification, with an accuracy of 78.96 percent and a f1-score of 78.81 percent. Changes in vector size affect the system's capacity to foresee positive and negative sentiments. The difference in accuracy and recall values shows that when vector size 300 is utilized, the precision and recall outcomes are lower than when vector size 500 is used.
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References
H. S. Batubara, Ambiyar, Syahril, Fadhilah, and R. Watrianthos, “Sentiment Analysis of Face-To-Face Learning Based on Social Media,” J. Pendidik. Teknol. Kejuru., vol. 4, no. 3, 2021.
Samsir, Ambiyar, U. Verawardina, F. Edi, and R. Watrianthos, “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes,” J. Media Inform. Budidarma, vol. 5, no. 1, pp. 157–163, 2021, doi: 10.30865/mib.v5i1.2604.
H. S. Batubara, M. Giatman, W. Simatupang, and R. Watrianthos, “Pemetaan Bibliometrik Terhadap Riset pada Sekolah Menengah Kejuruan Menggunakan VOSviewer,” Edukatif J. Ilmu Pendidik., vol. 4, no. 1, pp. 233–239, 2022.
N. G. Ramadhan and T. I. Ramadhan, “Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM,” Sinkron, vol. 7, no. 1, pp. 39–45, Jan. 2022, doi: 10.33395/sinkron.v7i1.11204.
P. Antinasari, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 12, pp. 1733–1741, 2017.
M. Mahyarani, A. Adiwijaya, S. Al Faraby, and M. Dwifebri, “Implementation of Sentiment Analysis Movie Review based on IMDB with Naive Bayes Using Information Gain on Feature Selection,” in 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA), Jul. 2021, pp. 99–103. doi: 10.1109/ICERA53111.2021.9538763.
P. H. Gunawan, T. D. Alhafidh, and B. A. Wahyudi, “The Sentiment Analysis of Spider-Man: No Way Home Film Based on IMDb Reviews,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 177–182, Feb. 2022, doi: 10.29207/resti.v6i1.3851.
S. M. Qaisar, “Sentiment Analysis of IMDb Movie Reviews Using Long Short-Term Memory,” in 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Oct. 2020, pp. 1–4. doi: 10.1109/ICCIS49240.2020.9257657.
K. Kumar, B. S. Harish, and H. K. Darshan, “Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method,” Int. J. Interact. Multimed. Artif. Intell., vol. 5, no. 5, p. 109, 2019, doi: 10.9781/ijimai.2018.12.005.
G. Karak, S. Mishra, A. Bandyopadhyay, P. R. S. Rohith, and H. Rathore, “Sentiment Analysis of IMDb Movie Reviews: A Comparative Analysis of Feature Selection and Feature Extraction Techniques,” in Hybrid Intelligent Systems, 2022, pp. 283–294. doi: 10.1007/978-3-030-96305-7_27.
R. Novendri, A. S. Callista, D. N. Pratama, and C. E. Puspita, “Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes,” Bull. Comput. Sci. Electr. Eng., vol. 1, no. 1, pp. 26–32, Jun. 2020, doi: 10.25008/bcsee.v1i1.5.
L. N, “IMDB Dataset of 50K Movie Reviews,” kaggle.com, 2019. https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews (accessed Apr. 02, 2022).
T. Adewumi, F. Liwicki, and M. Liwicki, “Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks,” Open Comput. Sci., vol. 12, no. 1, pp. 134–141, Mar. 2022, doi: 10.1515/comp-2022-0236.
Alvi Rahmy Royyan and Erwin Budi Setiawan, “Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 78–84, Feb. 2022, doi: 10.29207/resti.v6i1.3525.
Samsir et al., “Naives Bayes Algorithm for Twitter Sentiment Analysis,” J. Phys. Conf. Ser., vol. 1933, no. 1, p. 012019, Jun. 2021, doi: 10.1088/1742-6596/1933/1/012019.
R. Watrianthos, S. Suryadi, D. Irmayani, M. Nasution, and E. F. S. Simanjorang, “Sentiment analysis of traveloka app using naïve bayes classifier method,” Int. J. Sci. Technol. Res., vol. 8, no. 7, pp. 786–788, 2019, doi: 10.31227/osf.io/2dbe4.
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