Sentiment Analysis of Social Media Platform Reviews Using the Naïve Bayes Classifier Algorithm

Sudin Saepudin; Selviani Widiastuti; Carti Irawan

doi:10.32736/sisfokom.v12i2.1650

Authors

Sudin Saepudin Universitas Nusa Putra
Selviani Widiastuti Universitas Nusa Putra
Carti Irawan Universitas Nusa Putra

DOI:

https://doi.org/10.32736/sisfokom.v12i2.1650

Keywords:

Sentiment Analysis, Google Play Store, NBC (Naïve Bayes Classifier), Social Media Platforml, Python

Abstract

The Covid-19 pandemic has caused significant changes in people's lifestyles which are further strengthened by the rapid development of technology. This has resulted in increased use of the internet and accelerated dissemination of information through social media platforms. Not only for self-expression, social media can also be a means of communication, information, education, and even used as a marketing tool. Several social media platforms have recently been popular and widely used, the number of users is increasing from year to year, and each user can provide a rating review of the application. To find out public opinion on social media platforms, sentiment analysis will be carried out on several social media platform applications on the Google Play Store, namely Twitter, Instagram and Tiktok which will later be used as material for evaluating these applications. In this study, the dataset was taken based on ratings from user reviews on the Google Play Store using the NBC (Naïve Bayes Classifier) method with the Python programming language. Based on testing of 1000 comment review data from each application, it was found that the majority gave positive sentiment (Twitter 57.2%, Instagram 74.1%, Tiktok 83.9%), and negative sentiment (Twitter 42.8%, Instagram 25.9%, Tiktok 16.1%) with an accuracy rate of 85.6% for the Twitter application, 83.6% for the Instagram application, and 84.8% for the Tiktok application.

References

S. Fide, Suparti, and Sudarno, “Analisis Sentimen Ulasan Aplikasi Tiktok Di Google Play Menggunakan Metode Support Vector Machine (SVM) Dan Asosiasi,” J. Gaussian, vol. 10, no. 3, pp. 346–358, 2021.

S. M. Salsabila, A. A. Murtopo, and N. Fadhilah, “Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier,” J. Minfo Polgan, vol. 11, no. 02, 2022, [Online]. Available: www.tokopedia.com

C. F. Hasri and D. Alita, “Penerapan Metode Naive Bayes Classifier Dan Support Vector Machine Pada Analisis Sentimen Terhadap Dampak Virus Corona Di Twitter,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 3, no. 2, pp. 145–160, 2022, [Online]. Available: http://jim.teknokrat.ac.id/index.php/informatika

M. F. Al-shufi and A. Erfina, “Sentimen Analisis Mengenai Aplikasi Streaming Film Menggunakan Algoritma Support Vector Machine Di Play Store,” J. SISMATIK, vol. 01, no. 01, 2021, Accessed: Apr. 14, 2023. [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/22

N. Ramadhani and N. Fajarianto, “Sistem Informasi Evaluasi Perkuliahan dengan Sentimen Analisis Menggunakan Naïve Bayes dan Smoothing Laplace,” J. Sist. Inf. BISNIS, vol. 10, no. 2, pp. 228–234, Dec. 2020, doi: 10.21456/vol10iss2pp228-234.

M. T. Nitami and H. Februariyaniti, “Analisis Sentimen Ulasan Ekspedisi J&T Express Menggunakan Algoritma Naive Bayes,” Jurna Manaj. Inform. Sist. Inf., vol. 5, no. 1, 2022.

R. Wahyudi and G. Kusumawardhana, “Analisis Sentimen pada review Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine,” J. Inform., vol. 8, no. 2, 2021, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ji

C. Villavicencio, J. J. Macrohon, X. A. Inbaraj, J. H. Jeng, and J. G. Hsieh, “Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes,” Inf., vol. 12, no. 5, May 2021, doi: 10.3390/info12050204.

M. Wongkar and A. Angdresey, “Sentiment analysis using Naive Bayes Algorithm of the data crawler: Twitter,” in 2019 Fourth International Conference on Informatics and Computing (ICIC), IEEE, 2019, pp. 1–5.

S. Satriajati, S. B. Panuntun, and S. Pramana, “Implementasi Web Scraping Dalam Pengumpulan Berita Kriminal Pada Masa Pandemi Covid-19 Studi Kasus: Situs Berita detik.com,” Semin. Nas. Off. Stat., no. 01, 2020.

M. Djufri, “Penerapan Teknik Web Scraping Untuk Penggalian Potensi Pajak (Studi Kasus pada Online Market Place Tokopedia, Shopee dan Bukalapak),” J. BPPK, vol. 13, no. 02, pp. 65–75, 2020.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.

D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” J. TEKNO KOMPAK, vol. 15, no. 1, 2021.

R. P. Sidiq, B. A. Dermawan, and Y. Umaidah, “Sentimen Analisis Komentar Toxic pada Grup Facebook Game Online Menggunakan Klasifikasi Naïve Bayes,” J. Inform. Univ. Pamulang, vol. 5, no. 3, p. 356, Sep. 2020, doi: 10.32493/informatika.v5i3.6571.

B. Gunawan, H. S. Pratiwi, and E. E. Pratama, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,” JEPIN, vol. 4, no. 2, pp. 17–29, 2018, [Online]. Available: www.femaledaily.com

W. Irmayani, “Visualisasi Data Pada Data Mining Menggunakan Metode Klasifikasi Naive Bayes,” J. Khatulistiwa Inform., vol. 9, no. 1, 2021, [Online]. Available: www.bsi.ac.id

N. Purwati, R. Nurlistiani, and O. Devinsen, “Data Mining Dengan Algoritma Neural Network Dan Visualisasi Data Untuk Prediksi Kelulusan Mahasiswa,” J. Inform., vol. 20, no. 2, pp. 156–163, Dec. 2020, doi: 10.30873/ji.v20i2.2273.

Z. U. Siregar, R. R. A. Siregar, and R. Arianto, “Klasifikasi Sentiment Analysis Pada Komentar Peserta Diklat Menggunakan Metode K-Nearest Neighbor,” J. KILAT, vol. 8, no. 1, 2019.

W. Yulita, E. D. Nugroho, and M. H. Algifari, “Sentiment Analysis on Public Opinion About the Covid-19 Vaccine Using the Naïve Bayes Classifier Algorithm.” Jdmsi, 2021.

A. E. Sari, S. Widowati, and K. M. Lhaksmana, “Klasifikasi Ulasan Pengguna Aplikasi Mandiri Online di Google Play Store dengan Menggunakan Metode Information Gain dan Naive Bayes Classifier,” e-Proceeding Eng., vol. 6, no. 2, 2019.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 646, 2021.

D. Rusdiaman and D. Rosiyadi, “Analisa Sentimen Terhadap Tokoh Publik Menggunakan Metode Naive Bayes Classifier Dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 2502–7131, 2019.

G. L. A. K. Putra and G. P. P. A. Yasa, “Komik Sebagai Sarana Komunikasi Promosi Dalam Media Sosial,” J. NAWALA Vis., vol. 1, no. 1, 2019, [Online]. Available: https://jurnal.std-bali.ac.id/index.php/nawalavisual

A. E. Y. Wibawa, “Implementasi Platform Digital Sebagai Media Pembelajaran Daring Di MI Muhammadiyah PK Kartasura Pada Masa Pandemi Covid-19,” Berajah J., vol. 01, no. 02, 2021, [Online]. Available: www.belajar.id

D. S. Utami and A. Erfina, “Analisis Sentimen Objek Wisata Bali Di Google Maps Menggunakan Algoritma Naive Bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, pp. 418–427, 2022.