| Title: |
Sentiment Analysis on Shopee Xpress Delivery Time Reviews Using Support Vector Machine and Logistic Regression |
| Authors: |
Sewin Fathurrohman; Irfan Ricky Afandi; Irma Wahyuningtyas; Azis Styo Nugroho; Firman Noor Hasan |
| Source: |
IJID (International Journal on Informatics for Development), Vol 14, Iss 2 (2026) |
| Publisher Information: |
State Islamic University Sunan Kalijaga, 2026. |
| Publication Year: |
2026 |
| Collection: |
LCC:Electronic computers. Computer science; LCC:Economic growth, development, planning |
| Subject Terms: |
classification algorithms; customer satisfaction; machine learning; sentiment prediction; user sentiment; Electronic computers. Computer science; QA75.5-76.95; Economic growth, development, planning; HD72-88 |
| Description: |
This study examines user sentiment towards Shopee Xpress delivery times using machine learning techniques. We collected 497 reviews from platforms like X and the Google Play Store, leveraging the valuable feedback despite its unstructured and informal nature. After labelling 398 reviews for model training and reserving 99 for sentiment prediction, we implemented two classification algorithms: Support Vector Machine (SVM) and Logistic Regression. These models categorised sentiments into negative, neutral, and positive classes. Despite class imbalance in the training data, SVM outperformed Logistic Regression with an accuracy of 93%, demonstrating a more balanced performance across sentiment categories compared to Logistic Regression's 90% accuracy. Both models showed consistent sentiment prediction on new data. Our findings highlight the potential of sentiment analysis as a valuable tool for Shopee Xpress to understand customer perceptions and improve delivery experiences. By providing actionable insights, this study can inform logistics improvements and enhance customer satisfaction. Future research could benefit from collaborating with Shopee to access internal data and integrating additional data sources for more comprehensive insights, ultimately driving business growth and customer loyalty. This study contributes to the growing body of research on sentiment analysis in logistics and e-commerce. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2252-7834; 2549-7448 |
| Relation: |
https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/5073; https://doaj.org/toc/2252-7834; https://doaj.org/toc/2549-7448 |
| DOI: |
10.14421/ijid.2025.5073 |
| Access URL: |
https://doaj.org/article/8c511fa9382e4e65bf34fb9237cd9dcf |
| Accession Number: |
edsdoj.8c511fa9382e4e65bf34fb9237cd9dcf |
| Database: |
Directory of Open Access Journals |