Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learning
In Vietnam's rapidly expanding e-commerce landscape, there is a critical need for advanced tools that can effectively analyze customer feedback to boost satisfaction and loyalty. This paper introduces a two-step predictive framework merging deep learning and traditional machine learning to anal...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096824000843 |
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| Summary: | In Vietnam's rapidly expanding e-commerce landscape, there is a critical need for advanced tools that can effectively analyze customer feedback to boost satisfaction and loyalty. This paper introduces a two-step predictive framework merging deep learning and traditional machine learning to analyze Vietnamese e-commerce reviews. Utilizing a dataset of 10,021 reviews on Tiki, Shopee, Sendo, and Hasaki between 2015 and 2023, the framework first employs fine-tuned deep learning models like BERT and Bi-GRU to extract aspect-based sentiments from reviews, tailored for the nuances of the Vietnamese language. Subsequently, machine learning algorithms like XGBoost predict customer satisfaction by integrating sentiment analysis with e-commerce data such as product prices. Results show BERT and Bi-GRU yield over 70% sentiment accuracy, while XGBoost achieves 80%+ satisfaction prediction accuracy. This framework offers a potent solution for discerning customer sentiments and enhancing satisfaction in Vietnam's dynamic e-commerce landscape. |
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| ISSN: | 2667-0968 |