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All Journal INOVTEK Polbeng - Seri Informatika
Sugeng Setyabudi
Universitas Pembangunan Nasioanal Veteran Jawa Timur
Computer Science & IT

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Sentiment Analysis of Lazada Marketplace User Ratings with Naïve Bayes and Support Vector Machine Methods Sugeng Setyabudi; Enny Aryanny
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/sww8cg21

Abstract

This research analyzes the sentiment of Lazada Marketplace users using Naïve Bayes and Support Vector Machine (SVM) methods. This sentiment analysis is important to understand customer perceptions, which can help Lazada in developing service improvement strategies and business policies. Data was obtained through web scraping, resulting in 5,261 comments divided into 80% training data and 20% test data. The text preprocessing process includes cleaning, case folding, stopword removal, tokenizing, and stemming to ensure the data is cleaner and more structured. The test results show that SVM performs better with 75% accuracy, 74% precision, 86% recall, and 79% F1-score, compared to Naïve Bayes which has 72% accuracy, 75% precision, 76% recall, and 76% F1-score on positive sentiment. The confusion matrix evaluation shows that SVM is more consistent in classifying positive, neutral, and negative sentiments. Word cloud visualization on negative sentiment revealed that the main customer complaints were related to the app, late delivery, and product mismatch. These findings can be used by Lazada to improve service quality by improving the technical aspects of the app, optimizing logistics, as well as improving quality control of products sold by the store to increase customer satisfaction and user loyalty.
Co-Authors Enny Aryanny