Lazada Customer Review Sentiment Analysis with Sastrawi Stemmer and SVM-PSO to Understand User Response
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Dec 19, 2024
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Abstract
In the digital era, sentiment analysis plays a strategic role in understanding customer perceptions of products and services. This research aims to analyze customer review sentiment on the Lazada platform through the application of text processing techniques and machine learning algorithms. Data is taken from product reviews on the platform, which then undergoes a preprocessing stage, including tokenization, stopword removal, and stemming using the Sastrawi algorithm. Next, sentiment classification was performed using a Support Vector Machine optimized through the Particle Swarm Optimization (PSO) method. The research results showed that the combination of the Sastrawi stemmer method and SVM-PSO was able to achieve significant accuracy, namely 90.57%, an increase of 6.24% compared to previous research. These findings provide deep insights into customer perceptions and offer valuable guidance for decision-makers at Lazada in improving service quality and customer satisfaction. This study also underscores the importance of applying Natural Language Processing techniques and machine learning algorithms in sentiment analysis on e-commerce platforms, which have proven to produce more accurate outputs.
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References
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Djatmiko, F., Ferdiana, R., & Faris, M. (2019). A Review of Sentiment Analysis for Non-English Language. 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 448–451. https://doi.org/10.1109/ICAIIT.2019.8834552
Fahmi, S., Purnamawati, L., Shidik, G. F., Muljono, M., & Fanani, A. Z. (2020). Sentiment Analysis of Student Review in Learning Management System Based on Sastrawi Stemmer and SVM-PSO. 2020 International Seminar on Application for Technology of Information and Communication (ISemantic), 643–648. https://doi.org/10.1109/iSemantic50169.2020.9234291
Hassan, R., & Islam, Md. R. (2021). Impact of Sentiment Analysis in Fake Online Review Detection. 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 21–24. https://doi.org/10.1109/ICICT4SD50815.2021.9396899
Hermanto, H., Fahlapi, R., Kuntoro, A. Y., & Asra, T. (2024). Perbandingan Algoritma Klasifikasi Analisis Sentimen Pengguna Aplikasi Getcontact Dalam Pencegahan Penipuan Online. J-INTECH, 12(1), 158–167. https://doi.org/10.32664/j-intech.v12i1.1262
Huan, L., Jingqi, M., & Xingjian, L. (2020). Target tracking method based on the fusion of structured SVM and KCF algorithm. 2020 Chinese Control And Decision Conference (CCDC), 1174–1178. https://doi.org/10.1109/CCDC49329.2020.9164843
LADJAL, M., OUALI, M. A., & LASS, M. D. (2020). optimization of SVM parameters with hybrid PCA-PSO methods for water quality monitoring. 2020 International Conference on Electrical Engineering (ICEE), 1–6. https://doi.org/10.1109/ICEE49691.2020.9249881
Li, J., Zhao, Y., Gao, X., & Zhao, W. (2020). Feature Extraction and SVM Parameters Optimization Based on ICQPSO. 2020 International Conference on Computer Network, Electronic and Automation (ICCNEA), 329–332. https://doi.org/10.1109/ICCNEA50255.2020.00074
Liu, J., Zhu, X., & Zhang, Y. (2020). Application of DE-GWO-SVM Algorithm in Business Order Prediction Model. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS), 432–435. https://doi.org/10.1109/ICSESS49938.2020.9237714
Liu, L., Sha, Y., & Zhang, Q. (2020). Bearing fault diagnosis analysis based on improved adaptive search algorithms and SVMs. 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), 680–684. https://doi.org/10.1109/CACRE50138.2020.9230327
Mustopa, A., Hermanto, Anna, Pratama, E. B., Hendini, A., & Risdiansyah, D. (2020). Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization. 2020 Fifth International Conference on Informatics and Computing (ICIC), 1–7. https://doi.org/10.1109/ICIC50835.2020.9288655
Phan, H. T., Tran, V. C., Nguyen, N. T., & Hwang, D. (2020). Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model. IEEE Access, 8, 14630–14641. https://doi.org/10.1109/ACCESS.2019.2963702
Pramita, A. G., & Nugraha, F. (2024). Sistem Analisis Sentimen Produk Pada Aplikasi Lazada Menggunakan Metode Naïve Bayes. Jurnal Digit, 14(1), 23. https://doi.org/10.51920/jd.v14i1.362
Punitha, S., & Jeyakarthic, M. (2020). Particle Swarm Optimization based Classification Algorithm for Expert Prediction Systems. 2020 International Conference on Inventive Computation Technologies (ICICT), 671–675. https://doi.org/10.1109/ICICT48043.2020.9112392
Riyanto, V. A., & Santoso, D. B. (2024). Penerapan Model Support Vector Machine Pada Klasifikasi Sentimen Ulasan Aplikasi Lazada. JURASIK (Jurnal Riset Sistem Informasi Dan Teknik Informatika), 9(1).
Rusyanti, D., & Sembiring, F. (2022). Implementasi Algoritma K Means Untuk Sentimen Analisis Review Produk Pada E-Commerce Lazada. SEMINAR NASIONAL SISTEM INFORMASI & MANAJEMEN INFORMATIKA (SISMATIK 2024), 2.
Saputra, F. T., Wijaya, S. H., Nurhadryani, Y., & Defina. (2020). Lexicon Addition Effect on Lexicon-Based of Indonesian Sentiment Analysis on Twitter. 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 136–141. https://doi.org/10.1109/ICIMCIS51567.2020.9354269
Shuran, C., & Yian, L. (2020). Breast cancer diagnosis and prediction model based on improved PSO-SVM based on gray relational analysis. 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 231–234. https://doi.org/10.1109/DCABES50732.2020.00067
Yadav, S., & Saleena, N. (2020). Sentiment Analysis Of Reviews Using an Augmented Dictionary Approach. 2020 5th International Conference on Computing, Communication and Security (ICCCS), 1–5. https://doi.org/10.1109/ICCCS49678.2020.9277094
Zhang, X., Han, P., Xu, L., Zhang, F., Wang, Y., & Gao, L. (2020). Research on Bearing Fault Diagnosis of Wind Turbine Gearbox Based on 1DCNN-PSO-SVM. IEEE Access, 8, 192248–192258. https://doi.org/10.1109/ACCESS.2020.3032719