SENTIMENT CLASSIFICATION ON E-COMMERCE USER REVIEWS WITH NATURAL LANGUANGE PROCESSING (NLP) AND SUPPORT VECTOR MACHINE (SVM) METHODS

Jimmy Iqbal Wiranata Siregar; Andysah Putera Utama Siahaan; Muhammad Iqbal; Darmeli Nasution; Rian Farta Wijaya

Authors

Jimmy Iqbal Wiranata Siregar Universitas Pembangunan Panca Budi
Andysah Putera Utama Siahaan Universitas Pembangunan Panca Budi
Muhammad Iqbal Universitas Pembangunan Panca Budi
Darmeli Nasution Universitas Pembangunan Panca Budi
Rian Farta Wijaya Universitas Pembangunan Panca Budi

Keywords:

E-Commerce, Natural Language Processing (NLP), Support Vector Machine (SVM)

Abstract

This research aims to build a classification model that can categorize e-commerce user reviews into positive, negative, or neutral sentiments. By using NLP techniques to process the review text and SVM as a classification algorithm, it is expected that this model can provide high accuracy in determining user sentiment. Common words that do not contribute to sentiment analysis, such as “and,” “which,” “for”, are removed, and SVM is applied after the review data is transformed into vectors using the TF-IDF method. The SVM model will be trained using training data that has been labeled with sentiment.

References

I. Setiawan, S. Nirwan, and F. Maisya Amelia, “Rancangan Bangun Aplikasi Marketplace Bagi Usaha Mikro Kecil, Dan Menengah Berbasis Web (SUB MODUL : PEMBELIAN),” 2018.R. Arulmozhiyal and K. Baskaran, "Implementation of a Fuzzy PI Controller for Speed Control of Induction Motors Using FPGA," Journal of Power Electronics, vol. 10, pp. 65-71, 2010.

A. Alwendi, “Penerapan E-Commerce Dalam Meningkatkan Daya Saing Usaha,” Manajemen Bisnis, vol. 17, no. 3, pp. 317–325, 2020, [Online]. Available: http://journal.undiknas.ac.id/index.php/magister-manajemen/

Y. S. Mulyani, “E-Commerce Solusi Pemasaran UMKM Dalam Mengembangkan Industri Pariwisata Di Tengah Pademi Covid-19 (Studi Kasus UMKM di Kota Tasikmalaya),” Khasanah Ilmu - Jurnal Pariwisata Dan Budaya, vol. 12, no. 2, pp. 131–141, 2021, doi: 10.31294/khi.v12i2.11293.

E. Y. Nasution, P. Hariani, L. S. Hasibuan, and W. Pradita, “Perkembangan Transaksi Bisnis E-Commerce terhadap Pertumbuhan Ekonomi di Indonesia,” Jesya, vol. 3, no. 2, pp. 506–519, 2020, doi: 10.36778/jesya.v3i2.227.

J. Sintadi, “Analisis Pengaruh Brand Image, Harga, Dan Review Produk Terhadap Keputusan Pembelian Smartphone Samsung Pada Mahasiswa Undip Semarang,” Diponegoro Journal of Management, vol. 8, no. 2, pp. 33–43, 2019, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/djom

N. Annisa, M. A. Nasution, and A. Firah, “Pengaruh Atribut Produk Dan Informasi Produk Terhadap Keputusan Pembelian Pada Pt . Trimitra Swadaya Medan the Effect of Product Attributes and Product Information on Purchase Decisions At Pt . Trimitra,” no. 1, pp. 67–76, 2023.

Z. Alhaq, A. Mustopa, S. Mulyatun, and J. D. Santoso, “Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter,” Journal of Information System Management (JOISM), vol. 3, no. 2, pp. 44–49, 2021, doi: 10.24076/joism.2021v3i2.558.

N. Munasatya and S. Novianto, “Natural Language Processing untuk Sentimen Analisis Presiden Jokowi Menggunakan Multi Layer Perceptron,” Techno.Com, vol. 19, no. 3, pp. 237–244, 2020, doi: 10.33633/tc.v19i3.3630.

R. Yusuf, T. A. Saputri, and A. A. Wicaksono, “Penerapan Natural Language Processing Berbasis Virtual Assistant Pada Bagian Administrasi Akademik Stmik Dharma Wacana,” International Research on Big-Data and Computer Technology: I-Robot, vol. 5, no. 1, pp. 33–47, 2022, doi: 10.53514/ir.v5i1.228.

V. Amrizal, “Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim),” Jurnal Teknik Informatika, vol. 11, no. 2, pp. 149–164, 2018, doi: 10.15408/jti.v11i2.8623.

A. Wijaya, “Analisis Sentimen Kepuasan Pelanggan pada Penyedia Layanan Pengantaran Makanan,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 1699–1708, 2022, doi: 10.35957/jatisi.v9i3.2196.

Fadhilah Dwi, Ananda, and Y. Pristyanto, “Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 2, pp. 407–416, 2021, doi: 10.30812/matrik.v20i2.1130.

N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm),” J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.

M. Z. Farhan, “Analisis Sentimen Layanan Shopeefood Pada Twitter Dengan Metode K-Nearest Neighbor, Support Vector Machine, dan Decision Tree,” J. Ilm. Inform., vol. 7, no. 2, pp. 95–106, 2023, doi: 10.35316/jimi.v7i2.95-106.

K. A. Rokhman, Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online,” J. Inf. Syst. Manag., vol. 2, no. 2, 2021.

D. L. Rianti, Y. Umaidah, and A. Voutama, “Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 6, no. 1, p. 98, 2021, doi: 10.30998/string.v6i1.9993.