Beauty Product Recommendation from Customer Reviews Based on Multinomial Naïve Bayes Algorithm
DOI:
https://doi.org/10.36423/index.v7i1.2208Keywords:
Customer Sentiment, Concealer, Sentiment Analysis, Naïve Bayes Classification, Product DevelopmentAbstract
The cosmetic industry’s increasing dependence on online platforms makes understanding customer sentiment essential for brand success. The study addresses the challenge of understanding customer sentiment regarding concealer brands on the Shopee platform, a critical aspect for brand managers and consumers alike. The problem stems from the vast amount of reviews that can be overwhelming for stakeholders looking to extract actionable insights. To solve this, we applied a Naïve Bayes classification approach with a Bag of Words (BoW) model to analyze a dataset containing 3,920 customer reviews. This dataset was divided into 1,120 training samples and 280 testing samples of each of 10 brands, following an 80:20 ratio. The analysis yields recurring positive and negative sentiment themes, achieving test accuracy of 87.95% and a training accuracy of 94.31%. Findings reveal key consumer preferences and lead to specific product recommendations, such as Mad For Makeup and Luxcrime for high coverage, Guele for lightweight formulas. Additionally, tailored marketing strategies like enhancing packaging and engaging with consumers through social media are suggested. This research provides actionable insights for brand managers, contributing to sentiment analysis literature in the cosmetics sector.
References
Y. Pei, J. Niu, and K. Min, “Value co-destruction in live-commerce ecosystem: an evolutionary game approach with insights from China,” Electronic Commerce Research, 2025, doi: 10.1007/s10660-025-09957-3.
A. S. Sridhar and S. Nagasundaram, “Sentiment classification for review rating prediction based on hybrid reaction diffusion support vector neural network,” Soc Netw Anal Min, vol. 14, no. 1, p. 240, 2024, doi: 10.1007/s13278-024-01397-5.
A. Daza, N. D. González Rueda, M. S. Aguilar Sánchez, W. F. Robles Espíritu, and M. E. Chauca Quiñones, “Sentiment Analysis on E-Commerce Product Reviews Using Machine Learning and Deep Learning Algorithms: A Bibliometric Analysis, Systematic Literature Review, Challenges and Future Works,” International Journal of Information Management Data Insights, vol. 4, no. 2, p. 100267, 2024, doi: https://doi.org/10.1016/j.jjimei.2024.100267.
A. Ray et al., “Sentiment Analysis of Amazon Product Reviews: A Comprehensive Evaluation Using Naïve Bayes Classifiers,” in Computing and Machine Learning, J. C. Bansal, S. Borah, S. Hussain, and S. Salhi, Eds., Singapore: Springer Nature Singapore, 2024, pp. 347–358.
M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowl Based Syst, vol. 226, p. 107134, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107134.
Q. Lu, X. Sun, Y. Long, Z. Gao, J. Feng, and T. Sun, “Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges,” IEEE Trans Neural Netw Learn Syst, vol. 35, no. 11, pp. 15092–15112, 2024, doi: 10.1109/TNNLS.2023.3294810.
M. Al-Fayoumi and Q. Abu Al-Haija, “Capturing low-rate DDoS attack based on MQTT protocol in software Defined-IoT environment,” Array, vol. 19, p. 100316, 2023, doi: https://doi.org/10.1016/j.array.2023.100316.
A. H. Aljemely, J. Xuan, L. Xu, F. K. J. Jawad, and O. Al-Azzawi, “Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification,” Applied Intelligence, pp. 1–19, 2021.
T. Ige, C. Kiekintveld, A. Piplai, A. Waggler, O. Kolade, and B. H. Matti, “An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey,” ArXiv, vol. abs/2411.16751, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:274280486
S. Kumar, A. Sharma, B. K. Reddy, S. Sachan, V. Jain, and J. Singh, “An intelligent model based on integrated inverse document frequency and multinomial Naive Bayes for current affairs news categorisation,” International Journal of System Assurance Engineering and Management, vol. 13, no. 3, pp. 1341–1355, 2022, doi: 10.1007/s13198-021-01471-7.
M. Schonlau, “The Naive Bayes Classifier,” in Applied Statistical Learning: With Case Studies in Stata, M. Schonlau, Ed., Cham: Springer International Publishing, 2023, pp. 143–160. doi: 10.1007/978-3-031-33390-3_8.
A. Angdresey, L. Sitanayah, and I. L. H. Tangka, “Sentiment Analysis for Political Debates on YouTube Comments using BERT Labeling, Random Oversampling, and Multinomial Naïve Bayes,” Journal of Computing Theories and Applications, vol. 2, no. 3, pp. 342–354, 2025.
N. L. Adam, N. H. Rosli, and S. C. Soh, “Sentiment Analysis on Movie Review using Naïve Bayes,” in 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS), 2021, pp. 1–6. doi: 10.1109/AiDAS53897.2021.9574419.
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,” Information, vol. 12, no. 5, p. 204, 2021.
K. A. Hashim, Y. Sibaroni, and S. S. Prasetyowati, “The Effectiveness of the Ensemble Naive Bayes in Analyzing Review Sentiment of the Lazada Application on Google Play,” in 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 2024, pp. 1–5. doi: 10.1109/ICETSIS61505.2024.10459562.
F. Novitasari and M. Dwifebri Purbolaksono, “Sentiment Analysis Aspect Level on Beauty Product Reviews Using Chi-Square and Naïve Bayes,” JOURNAL OF DATA SCIENCE AND ITS APPLICATIONS, vol. 4, no. 1, pp. 18–30, 2021.
D. Marlina, K. Tri Basuki, Z. Mohd Zaki, and A. Siti Farahnasihah, “Sentiment Analysis on Natural Skincare Products,” Journal of Data Science, vol. 2022, no. 12, pp. 1–17, 2022.
C. Therence and J. Tjen, “Analysis of Cosmetic Product Opinions on E-commerce Based on Naïve Bayes Classifier,” in International Conference on Digital Business Innovation and Technology Management (ICONBIT), 2024.
N. Suryawanshi, “Sentiment analysis with machine learning and deep learning: A survey of techniques and applications,” International Journal of Science and Research Archive, vol. 12, pp. 5–15, Jun. 2024, doi: 10.30574/ijsra.2024.12.2.1205.
E. De Santis, A. Martino, F. Ronci, and A. Rizzi, “From Bag-of-Words to Transformers: A Comparative Study for Text Classification in Healthcare Discussions in Social Media,” IEEE Trans Emerg Top Comput Intell, vol. 9, no. 1, pp. 1063–1077, 2025, doi: 10.1109/TETCI.2024.3423444.
S. Juanita, A. H. Daeli, M. Syafrullah, W. Anggraeni, and M. H. Purnomo, “A machine learning approach for text pattern diagnosis in mental health consultations,” Decision Analytics Journal, p. 100572, 2025, doi: https://doi.org/10.1016/j.dajour.2025.100572.
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Copyright (c) 2025 Claretty Therence, Jimmy Tjen
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