Cosmetic Shop Sentiment Analysis on TikTok Shop Using the Support Vector Machine Method

Rahmawati Rahmawati, Wahyu Fuadi, Yesy Afrillia

Abstract


User reviews are crucial in today's digital world for determining a product's quality. Nevertheless, these remarks are frequently disorganized and erratic, which confuses people and makes it challenging for them to make wise purchases. The erratic character of these reviews breeds uncertainty and makes determining a product's actual value more difficult. To help consumers more effectively evaluate and select products on platforms such as TikTok Shop, this study uses sentiment analysis tools. It hopes to accomplish this by improving the overall shopping experience and empowering customers to make more confident and informed selections. This research aims to assist consumers in evaluating and selecting products on TikTok Shop, an online shopping platform, by employing sentiment analysis techniques that help consumers make more informed decisions. In this study, a total of 500 comments from TikTok Shop users were collected as data. 350 comments have been set aside for training and 150 comments were set aside for testing. Data was gathered employing scraping, an automated process that makes use of the Python library's Selenium module to retrieve data from the internet. We employed the Support Vector Machine approach, an efficient machine learning tool for text classification, to assess the comments. 121 comments were categorized as having positive sentiment and 29 as having negative sentiment based on the test results. The system successfully recommended the "Ourluxbeauty" cosmetics store as a shop with many positive sentiments, indicating a recommendation level of 0.7 on the positive sentiment scale. The system's accuracy was measured using a Confusion Matrix, resulting in an accuracy rate of 78% and an inaccuracy rate of 22%. This demonstrates that the system can accurately classify comment sentiments and has significant potential for application in e-commerce practices to enhance the online shopping experience.


Keywords


Analysis, Sentiment, TikTok Shop, Cosmetic Store, Support Vector Machine

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References


Muhammad Romzi and B. Kurniawan, “Pembelajaran Pemrograman Python Dengan Pendekatan Logika Algoritma,” JTIM J. Tek. Inform. Mahakarya, vol. 03, no. 2, pp. 37–44, 2020.

A. S. Yondra, D. Triyanto, and S. Bahri, “Implementasi Web Scraping untuk Mengumpulkan Informasi Produk dari Situs E-commerce dan Marketplace dengan Teknik Pemrosesan Paralel,” Coding J. Komput. dan Apl., vol. 10, no. 01, pp. 93–102, 2022.

A. J. Putri, A. S. Syafira, and M. E. Purbaya, “Analisis Sentimen E-Commerce Lazada pada Jejaring Sosial Twitter Menggunakan Algoritma Support Vector Machine,” vol. 01, no. 1, 2022.

J. A. Zulqornain and P. P. Adikara, “Analisis Sentimen Tanggapan Masyarakat Aplikasi Tiktok Menggunakan Metode Naïve Bayes dan Categorial Propotional Difference ( CPD ),” vol. 5, no. 7, pp. 2886–2890, 2021.

C. B. Dewa and L. A. Safitri, “Pemanfaatan Media Sosial Tiktok Sebagai Media Promosi Industri Kuliner Di Yogyakarta Pada Masa Pandemi Covid-19 (Studi Kasus Akun TikTok Javafoodie),” Khasanah Ilmu - J. Pariwisata Dan Budaya, vol. 12, no. 1, pp. 65–71, 2021, doi: 10.31294/khi.v12i1.10132.

A. Aldila Safitri, A. Rahmadhany, and I. Irwansyah, “Penerapan Teori Penetrasi Sosial pada Media Sosial: Pengaruh Pengungkapan Jati Diri melalui TikTok terhadap Penilaian Sosial,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 3, no. 1, pp. 1–9, Jan. 2021, doi: 10.47233/jteksis.v3i1.180.

Z. Fitra Ramadhan and A. Benny Mutiara, “Sentiment Analysis of Honkai: Star Rail Indonesian Language Reviews on Google Play Store Using Bidirectional Encoder Representations from Transformers Method,” Int. J. Eng. Sci. Inf. Technol., vol. 3, no. 3, pp. 1–6, 2023, doi: 10.52088/ijesty.v3i3.462.

Y. Afrillia, L. Rosnita, and D. Siska, “Analisis Sentimen Ciutan Twitter Terkait Penerapan Permendikbudristek Nomor 30 Tahun 2021 Menggunakan TextBlob dan Support Vector Machine,” G-Tech J. Teknol. Terap., vol. 6, no. 2, pp. 387–394, 2022, doi: 10.33379/gtech.v6i2.1778.

R. Refianti and N. Anggraeni, “Sentiment Analysis Using Convolutional Neural Network Method to Classify Reviews on Zoom Cloud Meetings Application Based on Reviews on Google Playstore,” vol. 3, no. 3, pp. 7–16, 2023.

M. Qamal et al., “ANALISIS SENTIMEN TOKO ONLINE MENGGUNAKAN dilakukan oleh Mehdi Mursalat Ismail dan Kemas Muslim Lhaksamana dengan judul ‘ Sen timen Analisis Pada Media Online Mengenai Pemilihan Presisen 2019 dengan Menggunakan Metode Naive Bayes ’,” no. 1.

Munirul, Ula, M. M. Alvanof, and R. Triandi, “Analisa Dan Deteksi Konten Hoax Pada Media Berita,” J. Teknol. Terap. Sains 4.0 Univ. Malikussaleh, vol. 1, p. 2, 2020.

H. Hartono et al., “A New Diversity Technique for Imbalance Learning Ensembles,” Int. J. Eng. Technol., 2018, doi: 10.14419/ijet.v7i2.11251.

Y. Afrillia, L. Rosnita, and D. Siska, “Analisis Sentimen Pengguna Twitter Terhadap Isu Kesetaraan Gender Dalam Penerapan Permendikbudristek Nomor 30 Tahun 2021,” J. Informatics …, vol. 8, no. 2, pp. 93–98, 2022.

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov., 1998, doi: 10.1023/A:1009715923555.

S. Shevira, I. M. A. D. Suarjaya, and P. W. Buana, “Pengaruh Kombinasi dan Urutan Pre-Processing pada Tweets Bahasa Indonesia,” JITTER J. Ilm. Teknol. dan Komput., vol. 3, no. 2, p. 1074, 2022, doi: 10.24843/jtrti.2022.v03.i02.p06.

A. Rahman Isnain, A. Indra Sakti, D. Alita, and N. Satya Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm,” Jdmsi, vol. 2, no. 1, pp. 31–37, 2021.

A. Apriani, H. Zakiyudin, and K. Marzuki, “Penerapan Algoritma Cosine Similarity dan Pembobotan TF-IDF System Penerimaan Mahasiswa Baru pada Kampus Swasta,” J. Bumigora Inf. Technol., vol. 3, no. 1, pp. 19–27, 2021, doi: 10.30812/bite.v3i1.1110.

Y. Kardila, “Analisis Sentimen Review Pengguna Website IMDB Menggunakan Klasifikasi Naïve Bayes.”

F. S. Jumeilah, “Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes,” J. Sist. Inf. Bisnis, vol. 8, no. 1, p. 92, 2018, doi: 10.21456/vol8iss1pp92-98.

D. A. Agustina, S. Subanti, and E. Zukhronah, “Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine,” Indones. J. Appl. Stat., vol. 3, no. 2, p. 109, 2021, doi: 10.13057/ijas.v3i2.44337.




DOI: https://doi.org/10.52088/ijesty.v4i2.498

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International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674