Sentiment Analysis Of Shopee Product Reviews Using Distilbert

TL;DR

This paper evaluates the use of DistilBERT for sentiment analysis on Shopee product reviews, demonstrating it offers a good balance of high accuracy and computational efficiency for large-scale e-commerce data.

Contribution

It introduces the application of DistilBERT to large-scale sentiment analysis of Shopee reviews, comparing its performance with BERT and SVM, and highlights its efficiency advantages.

Findings

DistilBERT achieved 94.8% accuracy on review sentiment classification.

It reduced computation time by over 55% compared to BERT.

DistilBERT outperformed SVM in accuracy and efficiency.

Abstract

The rapid growth of digital commerce has led to the accumulation of a massive number of consumer reviews on online platforms. Shopee, as one of the largest e-commerce platforms in Southeast Asia, receives millions of product reviews every day containing valuable information regarding customer satisfaction and preferences. Manual analysis of these reviews is inefficient, thus requiring a computational approach such as sentiment analysis. This study examines the use of DistilBERT, a lightweight transformer-based deep learning model, for sentiment classification on Shopee product reviews. The dataset used consists of approximately one million English-language reviews that have been preprocessed and trained using the distilbert-base-uncased model. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, and compared against benchmark models such as BERT and SVM. The results show that DistilBERT achieved an accuracy of 94.8%, slightly below BERT (95.3%) but significantly higher than SVM (90.2%), with computation time reduced by more than 55%. These findings demonstrate that DistilBERT provides an optimal balance between accuracy and efficiency, making it suitable for large scale sentiment analysis on e-commerce platforms. Keywords: Sentiment Analysis, DistilBERT, Shopee Reviews, Natural Language Processing, Deep Learning, Transformer Models.