Sentiment Analysis Of The Shopee Marketplace On Twitter Using The Naive Bayes Classifier Method
Abstract
Shopee is the number one most downloaded marketplace application on the App Store and Play Store. In its promotion, Shopee provides discounts on shipping costs, price discounts, and cashback for each transaction; however, not all of its users are satisfied with the service. There are criticisms and suggestions, one of which is conveyed via social media, Twitter. Sentiment analysis was conducted to extract information related to Shopee user reviews on Twitter. The stages of the research carried out followed the Cross Industry Standard Process for Data mining (CRISP-DM) method, namely Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data collection was carried out by scraping, and all classification processes were carried out using the RapidMiner tool. The data obtained tends to contain negative sentiment rather than positive; most reviews are made by buyers and discuss promos. Sentiment classification is carried out by applying the Naive Bayes Classifier and TF-IDF as feature extraction. Testing using 10-fold cross-validation and a Confusion Matrix resulted in an accuracy value of 84.20%, a precision value of 87.21%, and a recall value of 84.20%.
Full Text:
PDF (66-77)References
F. Febrianto and L. Huda, “IdEA: Kenaikan Penjualan E-commerce 25 Persen selama Pandemi,” TEMPO, 2020. https://www.tempo.co/ekonomi/idea-kenaikan-penjualan-e-commerce-25-persen-selama-pandemi--565643
M. P. Andriani and E. Prihantoro, “Analysis of Business Communication Strategy for Information Technology Lazada Indonesia,” J. Pewarta Indones., vol. 4, no. 1, pp. 41–59, 2022, doi: 10.25008/jpi.v4i1.102.
F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” J. SIMETRIS, vol. 10, no. 2, pp. 681–686, 2019.
W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014, doi: 10.1016/j.asej.2014.04.011.
A. Saleh, “Klasifikasi Metode Naive Bayes Dalam Data Mining Untuk Menentukan Konsentrasi Siswa,” KeTIK, pp. 200–208, 2015.
D. Rustiana and N. Rahayu, “Analisis sentimen pasar otomotif mobil:,” J. SIMETRIS, vol. 8, no. 1, pp. 113–120, 2017.
M. L. K. Harsono, Y. Alkhalifi, Nurajijah, and W. Gata, “Analisis Sentimen Stakeholder atas Layanan hai DJPbn,” Infoman’s - J. Ilmu-Ilmu Inform. dan Manaj., vol. 14, no. 1, pp. 1–9, 2020, doi: 10.33481/infomans.v14i1.126.
A. Z. Saifinnuha, “Penerapan Sentimen Analisis Pada Twitter Berbahasa Indonesia untuk Mendapatkan Rating Program Televisi Menggunakan Metode Support Vector Machine : Skripsi,” Universitas Brawijaya, 2015.
E. H. Muktafin, K. Kusrini, and E. T. Luthfi, “Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing,” J. Eksplora Inform., vol. 10, no. 1, pp. 32–42, 2020, doi: 10.30864/eksplora.v10i1.390.
M. M. Saritas and A. Yasar, “Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification,” Int. J. Intell. Syst. Appl. Eng., vol. 7, no. 2, 2019, doi: 10.1039/b000000x.
F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive Bayes pada Penentuan Status Gunung Berapi dengan K-Fold Cross Validation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 577–584, 2018, doi: 10.25126/jtiik.201855983.
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf. Sci. (Ny)., vol. 507, pp. 772–794, 2020, doi: 10.1016/j.ins.2019.06.064.
I. Saputra and D. A. Kristiyanti, Machine Learning untuk Pemula, Cetakan Pe. Bandung: Informatika Bandung, 2022.
D. Aprilla C, D. A. Baskoro, L. Ambarwati, and I. W. S. Wicaksana, “Belajar Data Mining Dengan Rapid Minner,” p. 139, 2013, [Online]. Available: https://www.academia.edu/7712860/Belajar_Data_Mining_dengan_RapidMiner
I. N. Sjamsuddin, D. A. Firmansyah, and Y. Laferani, “Klasterisasi Pasien Rawat Inap BPJS pada RS Islam Assyifa Sukabumi menggunakan Metode K-Means,” J. Kridatama Sains dan Teknol., vol. 7, no. 01, pp. 355–368, 2025, doi: 10.53863/kst.v7i01.1617.
DOI: https://doi.org/10.37058/jaisi.v3i2.17077
Refbacks
- There are currently no refbacks.
International Journal of Applied Information Systems and Informatics (JAISI)
Department of Information Systems, Faculty of Engineering, Siliwangi University Tasikmalaya
email: jaisi@unsil.ac.id
Jalan Siliwangi No. 24 Kelurahan Kahuripan Kecamatan Tawang Kota Tasikmalaya 46115
This work is licenced under a Creative Commons Attribution 4.0 International Licence