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Sentiment Analysis of Shopee App Usage Reviews Using Metaheuristic Algorithm Optimization.

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Bibliographic Details
Title: Sentiment Analysis of Shopee App Usage Reviews Using Metaheuristic Algorithm Optimization.
Authors: Yusmanidar, Cipta, Hendra, Nasution, Rini Halila
Source: EduMatSains Jurnal Pendidikan Matematika dan Sains; Jan2026, Vol. 10 Issue 3, p365-376, 12p
Subject Terms: SENTIMENT analysis, PARTICLE swarm optimization, FEATURE extraction, CONSUMERS' reviews, SUPPORT vector machines, ELECTRONIC commerce, CLASSIFICATION, METAHEURISTIC algorithms
Abstract: Sentiment analysis is widely used to classify opinions expressed in textual data; however, many previous studies employing Support Vector Machine (SVM) do not explicitly address parameter optimization, which can lead to suboptimal classification performance. To address this research gap, this study integrates Particle Swarm Optimization (PSO) to optimize key SVM parameters for sentiment analysis of Shopee application reviews. The dataset consists of 1,000 Indonesian language user reviews collected from the Google Play Store between January and August 2025. Text preprocessing was conducted prior to feature extraction using the Term Frequency–Inverse Document Frequency (TFIDF) method. PSO was applied to optimize the kernel parameters, penalty parameter (C), and gamma value of the SVM model. The results demonstrate that PSO-based optimization significantly improves classification performance, increasing accuracy from 86.0% to 90.0%, precision from 86.9% to 90.6%, recall from 87.6% to 91.2%, and F1- score from 87.2% to 90.9%. Additionally, positive reviews are dominated by keywords such as "cheap," "fast," and "free shipping," while negative reviews frequently contain terms such as "error" and "slow." These findings confirm that PSO effectively enhances SVM performance and provides reliable insights into user sentiment toward e-commerce applications. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Sentiment analysis is widely used to classify opinions expressed in textual data; however, many previous studies employing Support Vector Machine (SVM) do not explicitly address parameter optimization, which can lead to suboptimal classification performance. To address this research gap, this study integrates Particle Swarm Optimization (PSO) to optimize key SVM parameters for sentiment analysis of Shopee application reviews. The dataset consists of 1,000 Indonesian language user reviews collected from the Google Play Store between January and August 2025. Text preprocessing was conducted prior to feature extraction using the Term Frequency–Inverse Document Frequency (TFIDF) method. PSO was applied to optimize the kernel parameters, penalty parameter (C), and gamma value of the SVM model. The results demonstrate that PSO-based optimization significantly improves classification performance, increasing accuracy from 86.0% to 90.0%, precision from 86.9% to 90.6%, recall from 87.6% to 91.2%, and F1- score from 87.2% to 90.9%. Additionally, positive reviews are dominated by keywords such as "cheap," "fast," and "free shipping," while negative reviews frequently contain terms such as "error" and "slow." These findings confirm that PSO effectively enhances SVM performance and provides reliable insights into user sentiment toward e-commerce applications. [ABSTRACT FROM AUTHOR]
ISSN:25277642
DOI:10.33541/edumatsains.v10i3.7824