BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce
Abstract Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00747-1 |
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| _version_ | 1823861647016460288 |
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| author | Muhammad Rizwan Rashid Rana Asif Nawaz Saif Ur Rehman Muhammad Ali Abid Mubariz Garayevi Jana Kajanová |
| author_facet | Muhammad Rizwan Rashid Rana Asif Nawaz Saif Ur Rehman Muhammad Ali Abid Mubariz Garayevi Jana Kajanová |
| author_sort | Muhammad Rizwan Rashid Rana |
| collection | DOAJ |
| description | Abstract Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive work environment. These NLP-driven deep learning models offer valuable tools for E-Commerce HR and sales departments, enabling monitoring employee and users’ sentiment trends over time and assisting in implementing targeted interventions. Focusing on the e-commerce industry, this work utilizes NLP-driven deep learning methodologies to analyze employee and user feedback, aiming to identify sentiments. The proposed NLP-driven, deep learning-based framework is designed to classify user feedback into positive, negative, or neutral sentiments. The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. The results demonstrate the framework’s efficacy, achieving an impressive 93.35% accuracy rate, surpassing existing benchmark methods. The research significantly benefits e-commerce by refining product portfolios and enhancing workplace culture. |
| format | Article |
| id | doaj-art-75f72ff164f547adae2661fbb389e9bf |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-75f72ff164f547adae2661fbb389e9bf2025-02-09T12:53:47ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-02-0118111810.1007/s44196-025-00747-1BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-CommerceMuhammad Rizwan Rashid Rana0Asif Nawaz1Saif Ur Rehman2Muhammad Ali Abid3Mubariz Garayevi4Jana Kajanová5University Institute of Information Technology, PMAS-Arid Agriculture UniversityUniversity Institute of Information Technology, PMAS-Arid Agriculture UniversityUniversity Institute of Information Technology, PMAS-Arid Agriculture UniversityDepartment of Artificial Intelligence, University of AgricultureDepartment of Mathematics, College of Science, King Saud UniversityFaculty of Management, Comenius University BratislavaAbstract Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive work environment. These NLP-driven deep learning models offer valuable tools for E-Commerce HR and sales departments, enabling monitoring employee and users’ sentiment trends over time and assisting in implementing targeted interventions. Focusing on the e-commerce industry, this work utilizes NLP-driven deep learning methodologies to analyze employee and user feedback, aiming to identify sentiments. The proposed NLP-driven, deep learning-based framework is designed to classify user feedback into positive, negative, or neutral sentiments. The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. The results demonstrate the framework’s efficacy, achieving an impressive 93.35% accuracy rate, surpassing existing benchmark methods. The research significantly benefits e-commerce by refining product portfolios and enhancing workplace culture.https://doi.org/10.1007/s44196-025-00747-1Sentiment analysisNatural language processingDeep learningCustomer careE-commerce |
| spellingShingle | Muhammad Rizwan Rashid Rana Asif Nawaz Saif Ur Rehman Muhammad Ali Abid Mubariz Garayevi Jana Kajanová BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce International Journal of Computational Intelligence Systems Sentiment analysis Natural language processing Deep learning Customer care E-commerce |
| title | BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce |
| title_full | BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce |
| title_fullStr | BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce |
| title_full_unstemmed | BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce |
| title_short | BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce |
| title_sort | bert bigru senti gcn an advanced nlp framework for analyzing customer sentiments in e commerce |
| topic | Sentiment analysis Natural language processing Deep learning Customer care E-commerce |
| url | https://doi.org/10.1007/s44196-025-00747-1 |
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