Sentiment Analysis of Skincare Products Using the Naive Bayes Method

Karina Nurfebia; Sriani Sriani

doi:10.51519/journalisi.v6i3.817

Karina Nurfebia North Sumatera State Islamic University, Indonesia https://orcid.org/0009-0009-2439-5952
Sriani Sriani North Sumatera State Islamic University, Indonesia

DOI: 10.51519/journalisi.v6i3.817

Keywords: Data Mining, Sentiment Analysis, Skincare, Naïve Bayes

Abstract

The number of reviews about skincare products can be used as an evaluation of product quality and satisfaction from consumers who have used it as well as considerations for other consumers to try the product. With the number of reviews, it is important to classify reviews into positive, negative, and neutral classes so that the level of product quality from each classification class can be known. The number of reviews causes the review classification process to be unable to be carried out automatically, so sentiment analysis is carried out. To determine the classification of positive sentiment, negative sentiment, and neutral sentiment on the skincare product, the Naive Bayes algorithm method is used. Naive Bayes was chosen because it is easy to implement and has a probability value to classify data. To determine the percentage of results from the specified classification, the Confusion Matrix will be used. The results of the classification process using the Naive Bayes method produce data into 3 types, namely 65 positive classes, 87 neutral classes, and 24 negative classes with an accuracy value of 73%, precision 77%, recall 61%, and f1-score 63%.

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References

M. Hamka, N. Alfatari, and D. Ratna Sari, “Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier,” J. Sist. Komput. dan Inform., vol. 4, no. 1, p. 64, 2022, doi: 10.30865/json.v4i1.4740.

T. Astuti and Y. Astuti, “Analisis Sentimen Review Produk Skincare Dengan Naïve Bayes Classifier Berbasis Particle Swarm Optimization (PSO),” J. Media Inform. Budidarma, vol. 6, no. 4, p. 1806, 2022, doi: 10.30865/mib.v6i4.4119.

D. N. Sari, D. N. Sari, F. Adelia, F. Rosdiana, B. B. Butar, and M. Hariyanto, “Analisa Sentimen Terhadap Review Produk Kecantikan Menggunakan Metode Naive Bayes Classifier,” JIKA (Jurnal Inform., vol. 4, no. 3, p. 109, 2020, doi: 10.31000/jika.v4i3.3086.

S. Zeybek, E. Koç, and A. Seçer, “MS-TR : A Morphologically enriched sentiment Treebank and recursive deep models for compositional semantics in Turkish MS-TR : A Morphologically enriched sentiment Treebank and recursive deep models for compositional semantics in Turkish,” Cogent Eng., vol. 8, no. 1, 2021, doi: 10.1080/23311916.2021.1893621.

R. Nurhidayat and K. E. Dewi, “Penerapan Algoritma K-Nearest Neighbor Dan Fitur Ekstraksi N-Gram Dalam Analisis Sentimen Berbasis Aspek,” Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 1, pp. 91–100, 2023, doi: 10.34010/komputa.v12i1.9458.

S. K. Wardani and Y. A. Sari, “Analisis Sentimen menggunakan Metode Naïve Bayes Classifier terhadap Review Produk Perawatan Kulit Wajah menggunakan Seleksi Fitur N-gram dan Document Frequency Thresholding,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 12, pp. 5582–5590, 2021.

R. K. Botchway, A. B. Jibril, Z. K. Oplatková, and M. Chovancová, “Deductions from a Sub-Saharan African Bank’s Tweets: A sentiment analysis approach,” Cogent Econ. Financ., vol. 8, no. 1, 2020, doi: 10.1080/23322039.2020.1776006.

W. Van Atteveldt, M. A. C. G. Van Der Velden, and M. Boukes, “The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms,” Commun. Methods Meas., vol. 15, no. 2, pp. 121–140, 2021, doi: 10.1080/19312458.2020.1869198.

H. Fang, G. Jiang, and D. Li, “Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews,” Syst. Sci. Control Eng., vol. 10, no. 1, pp. 802–810, 2022, doi: 10.1080/21642583.2022.2123060.

K. W. Trisna and H. J. Jie, “Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review,” Appl. Artif. Intell., vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2014186.

N. S. Fathullah, Y. A. Sari, and P. P. Adikara, “Analisis Sentimen Terhadap Rating dan Ulasan Film dengan menggunakan Metode Klasifikasi Naïve Bayes dengan Fitur Lexicon-Based,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 2, pp. 590–593, 2020.

K. Ramasamy, A. Sundaramurthy, and D. Velusamy, “Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier,” Automatika, vol. 64, no. 4, pp. 783–797, 2023, doi: 10.1080/00051144.2023.2218164.

M. Furqan, M. Ikhsan, and R. Aini, “Algoritma Support Vector Machine Untuk Analisis Sentimen Masyarakat Indonesia Terhadap Pandemi Virus Corona Di Media Sosial,” Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 4, no. 4, pp. 908–915, 2023.

M. Furqan, S. Sriani, and S. M. Sari, “Analisis Sentimen Menggunakan K-Nearest Neighbor Terhadap New Normal Masa Covid-19 Di Indonesia,” Techno.Com, vol. 21, no. 1, pp. 51–60, 2022, doi: 10.33633/tc.v21i1.5446.

A. F. Setyaningsih, D. Septiyani, and S. R. Widiasari, “Implementasi Algoritma Naïve Bayes untuk Analisis Sentimen Masyarakat pada Twitter mengenai Kepopuleran Produk Skincare di Indonesia,” J. Teknol. Inform. dan Komput., vol. 9, no. 1, pp. 224–235, 2023, doi: 10.37012/jtik.v9i1.1409.

K. S. Kumar et al., “Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification Sentiment Analysis of Short Texts Using SVMs and,” Appl. Artif. Intell., vol. 38, no. 1, 2024, doi: 10.1080/08839514.2024.2321555.

F. Westin, “Time Period Categorization in Fiction : A Comparative Analysis of Machine Learning Techniques Time Period Categorization in Fiction : A Comparative,” Cat. Classif. Q., vol. 62, no. 2, pp. 124–153, 2024, doi: 10.1080/01639374.2024.2315548.

O. C. Edo et al., “Fintech adoption dynamics in a pandemic: An experience from some financial institutions in Nigeria during COVID-19 using machine learning approach,” Cogent Bus. Manag., vol. 10, no. 2, 2023, doi: 10.1080/23311975.2023.2242985.

V. Ganganwar and R. Rajalakshmi, “Employing synthetic data for addressing the class imbalance in aspect-based sentiment classification,” J. Inf. Telecommun., vol. 8, no. 2, pp. 167–188, 2024, doi: 10.1080/24751839.2023.2270824.