Detecting Bot Comments on a Product in Shopee Using the Gradient Boosting Method

Manda Sari; Rizal Rizal; Sujacka Retno

doi:10.51967/tanesa.v26i1.3344

Authors

Manda Sari Malikussaleh University
Rizal Malikussaleh University
Sujacka Retno Malikussaleh University

DOI:

https://doi.org/10.51967/tanesa.v26i1.3344

Keywords:

Bot Comment Detection, Gradient Boosting, E-commerce, Shopee, Text Classification

Abstract

Shopee is one of the largest e-commerce platforms in Southeast Asia, providing a product comment feature that serves as a primary reference for prospective buyers to assess product quality and seller reputation. Unfortunately, the prevalence of fake comments generated by bots—characterized by rigid language, repetitive patterns, and excessive praise—raises concerns about the authenticity of available reviews. This issue can negatively influence consumers’ purchasing decisions. This study aims to develop an automated system capable of detecting bot comments using the Gradient Boosting algorithm. A total of 3,000 comments were manually collected from various product categories and labeled directly by the researchers. The comment data were then processed through several stages, including text cleaning, tokenization, and lemmatization, to prepare for model analysis. The trained model demonstrated excellent performance, achieving an accuracy of 94.09%, precision of 95.99%, recall of 83.23%, and an F1-score of 89.13%. Based on these results, it can be concluded that the Gradient Boosting algorithm is highly effective in classifying bot comments and can help improve consumer trust and security in online shopping.

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