E-Commerce Product Review Sentiment Analysis: A Comparative Study of Naïve Bayes Classifier and Random Forest Algorithms on Marketplace Platforms

Authors

  • Cian Ramadhona Hassolthine Universitas Siber Asia
  • Toto Haryanto School of Data Science, Mathematics and Informatics, IPB University
  • Fenina Adline Twince Tobing School of Data Science, Mathematics and Informatics, IPB University
  • Muhammad Ikhwani Saputra School of Data Science, Mathematics and Informatics, IPB University

DOI:

https://doi.org/10.31937/ijnmt.v12i1.4246

Abstract

Achieving customer satisfaction and trust is a major challenge for success in the business world. Entrepreneurs must identify problems that arise from reviews given by customers. However, reading and sorting each review is time-consuming and considered inefficient. In order to overcome this, a study was conducted that aims to analyze sentiment on products sold in the Shopee marketplace using the Naïve Bayes Classifier and Random Forest algorithms. The focus of this study is on product reviews from XYZ Store. The main objective of this study is to determine a more accurate and efficient algorithm in classifying review sentiment, which can help companies in marketing strategies and product development. The results of this study can provide insight for companies about consumer responses to marketed products, so that they can be used as a basis for making strategic decisions to improve the quality of services and products. The results of the Random Forest method classification produce superior predictions compared to the Naïve Bayes Classifier method with an accuracy value of 92.5%, precision of 93%, Recall of 92.5% and F1-Score of 90%.

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Published

2025-07-15

How to Cite

Hassolthine, C. R., Haryanto, T., Adline Twince Tobing, F., & Ikhwani Saputra, M. (2025). E-Commerce Product Review Sentiment Analysis: A Comparative Study of Naïve Bayes Classifier and Random Forest Algorithms on Marketplace Platforms. IJNMT (International Journal of New Media Technology), 12(1), 55–60. https://doi.org/10.31937/ijnmt.v12i1.4246

Issue

Vol. 12 No. 1 (2025): Vol 12 No 1 (2025): IJNMT (International Journal of New Media Technology)

Section

Articles

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Copyright (c) 2025 Cian Ramadhona Hassolthine, Toto Haryanto, Fenina Adline Twince Tobing, Muhammad Ikhwani Saputra

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