Naive Bayes Algorithm for Sentiment Analysis on Spider-Man Movie: No Way Home: Data Mining

Ziddan  Makarim; Ismasari  Nawangsih; Sanudin Sanudin

doi:10.47709/cnahpc.v6i4.4845

Authors

Ziddan Makarim Universitas Pelita Bangsa
Ismasari Nawangsih Universitas Pelita Bangsa
Sanudin Universitas Pelita Bangsa

DOI:

10.47709/cnahpc.v6i4.4845

Keywords:

Movie Industry, Naive Bayes Algorithm, Sentiment Analysis, Spider-Man: No Way Home, Streaming Platform

Dimension Badge Record

Abstract

The rapid development of streaming platforms has significantly changed the landscape of movie consumption. The ease of access and social interaction in online communities has led to the creation of a new pop culture around movies. One interesting phenomenon is the movie Spider-Man: No Way Home, which sparked heated and viral conversations on various social media platforms. This research aims to analyze audience sentiment towards the movie Spider-Man: No Way Home using Naïve Bayes algorithm. Review data collected from online platforms was processed to identify positive and negative sentiments. The choice of Naïve Bayes algorithm is based on its efficiency and ability to classify text. The results showed that the model built was able to classify sentiment with an accuracy of 72.34%. The model is more effective in identifying positive reviews than negative, indicating a positive response from the majority of viewers. However, the model still needs to improve its performance in classifying negative sentiments. This research makes an important contribution in understanding audience preferences and evaluating the success of a movie, especially in the context of the digital era. The results can be utilized by the film industry to improve production quality, marketing strategies, and content development that is more relevant to audience preferences. In addition, this research also opens up opportunities for further development, such as the use of more complex algorithms or combining with other sentiment analysis techniques, as well as application to various types of social media content.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 46 times

References

Anwar, K. (2022). KLIK: Kajian Ilmiah Informatika dan Komputer Analisa sentimen Pengguna Instagram Di Indonesia Pada Review Smartphone Menggunakan Naive Bayes. 2(4), 148–155. https://djournals.com/klik

Darmawan, G., Alam, S., Imam Sulistyo, M., Studi Teknik Informatika, P., Tinggi Teknologi Wastukancana Purwakarta, S., & Artikel, R. (2023). ANALISIS SENTIMEN BERDASARKAN ULASAN PENGGUNA APLIKASI MYPERTAMINA PADA GOOGLE PLAYSTORE MENGGUNAKAN METODE NAÏVE BAYES INFO ARTIKEL ABSTRAK. 2(3), 100–108. https://doi.org/10.55123

Darwis, D., Shintya Pratiwi, E., Ferico, A., & Pasaribu, O. (2020). PENERAPAN ALGORITMA SVM UNTUK ANALISIS SENTIMEN PADA DATA TWITTER KOMISI PEMBERANTASAN KORUPSI REPUBLIK INDONESIA. In Jurnal Ilmiah Edutic (Vol. 7, Issue 1).

Darwis, D., Siskawati, N., & Abidin, Z. (n.d.). Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional. 15(1).

Esa Tiffani, I. (n.d.). Optimization of Naïve Bayes Classifier By Implemented Unigram, Bigram, Trigram for Sentiment Analysis of Hotel Review.

Felicia Watratan, A., Puspita, A. B., Moeis, D., Informasi, S., & Profesional Makassar, S. (2020). Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia. In JOURNAL OF APPLIED COMPUTER SCIENCE AND TECHNOLOGY (JACOST) (Vol. 1, Issue 1). http://journal.isas.or.id/index.php/JACOST

Khotimah, A. C., & Utami, E. (2022). COMPARISON NAÏVE BAYES CLASSIFIER, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE IN THE CLASSIFICATION OF INDIVIDUAL ON TWITTER ACCOUNT. Jurnal Teknik Informatika (JUTIF), 3(3). https://doi.org/10.20884/1.jutif.2022.3.3.254

Kokom Komariyah, Rahaditya Dasuki, Dias Bayu Saputra, Saeful Anwar, & Gifthera Dwilestari. (2020). Klasifikasi Stok Barang Menggunakan Algoritma Naïve Bayes Pada Pt.Dharma Electrindo Manufacturing. KOPERTIP?: Jurnal Ilmiah Manajemen Informatika Dan Komputer, 4(2), 35–41. https://doi.org/10.32485/kopertip.v4i2.117

Mulyani, S., & Novita, R. (2022). IMPLEMENTATION OF THE NAIVE BAYES CLASSIFIER ALGORITHM FOR CLASSIFICATION OF COMMUNITY SENTIMENT ABOUT DEPRESSION ON YOUTUBE. Jurnal Teknik Informatika (Jutif), 3(5), 1355–1361. https://doi.org/10.20884/1.jutif.2022.3.5.374

Najjichah, H., Syukur, A., & Subagyo, H. (2019). PENGARUH TEXT PREPROCESSING DAN KOMBINASINYA PADA PERINGKAS DOKUMEN OTOMATIS TEKS BERBAHASA INDONESIA. In Jurnal Teknologi Informasi (Vol. 15, Issue 1). http://research.

Noviansyah, B., Makmun Effendi, M., & Achmad, Y. (2024). Sentiment Analysis of Oppenheimer Movie Reviews: Naïve Bayes Algorithm for Public Opinion. Architecture and High Performance Computing, 6(3). https://doi.org/10.47709/cnapc.v6i3.4393

Retnosari, R., Studi, P., Informasi, S., Nusa, S., & Jakarta, M. (n.d.). ANALISIS KELAYAKAN KREDIT USAHA MIKRO BERJALAN PADA PERBANKAN DENGAN METODE NAIVE BAYES.

Safira, A., Masyarakat…?, A. S., & Hasan, F. N. (2023). ANALISIS SENTIMEN MASYARAKAT TERHADAP PAYLATER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER. Jurnal Sistem Informasi, 5(1).

Sundara, T. A., & Ekaputri Arnas, S. (n.d.). Naïve Bayes Classifier untuk Analisis Sentimen Isu Radikalisme.

Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, & Fitri Nurapriani. (2023). Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN. Jurnal KomtekInfo, 1–7. https://doi.org/10.35134/komtekinfo.v10i1.330

Yuyun, Nurul Hidayah, & Supriadi Sahibu. (2021). Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 820–826. https://doi.org/10.29207/resti.v5i4.3146