2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 190-201, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Nasser Nammas Albogami 1 *
Doi: https://doi.org/10.54216/FPA.160212
The emergence of e-commerce is introduced in the golden era. E-commerce product reviews are comments generated by customers of online shopping to estimate the service and product qualities having purchased; these remarks aid users in identifying the facts of the product. The sentiment polarity of e-commerce product analyses is the optimal method to get consumer opinions on a service or product. Hence, sentiment analysis (SA) of product remarks on e-commerce platforms is much more influential. Deep learning (DL) analysis of online consumer feedback can identify user behavior toward a sustainable future. Artificial intelligence (AI) can acquire perceptions from product evaluations to develop efficient products. The main challenge is that numerous ethical products do not satisfy customers’ expectations owing to the gap among users’ expectations and their perception of sustainable products. This paper focuses on the design of the Fusion of Artificial Intelligence Deep Learning Model for Product Reviews on E-Commerce (FAIDLM-PREC) model. The main intention of FAIDLM-PREC method is to appropriately distinguish the dissimilar types of sentiments that occur in the e-commerce reviews. Initially, data preprocessing is executed to increase the product review quality with Glove based word embedding method. For product reviews classification, the FAIDLM-PREC approach evolves fusion of dual DL methods namely Bidirectional Long Short‐Term Memory (Bi-LSTM) and gated recurrent unit (GRU) methods. Eventually, the parameters relevant to the two DL methods are perfectly modified utilizing the Archimedes optimization algorithm (AOA). An extensive experiment of the FAIDLM-PREC technique was conducted utilizing customer review database and outcomes indicated that the FAIDLM-PREC technique highlighted betterment over other recent methods to several measures.
Archimedes Optimization Algorithm , Gated Recurrent Unit , Bidirectional Long Short‐Term Memory , Product Reviews , Artificial Intelligence
[1] Onan, A., 2021. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency and Computation: Practice and Experience, 33(23), p.e5909.
[2] Al-Smadi, M., Al-Ayyoub, M., Jararweh, Y. and Qawasmeh, O., 2019. Enhancing aspect-based sentiment analysis of Arabic hotels’ reviews using morphological, syntactic and semantic features. Information Processing & Management, 56(2), pp.308-319.
[3] Oh, S., Ji, H., Kim, J., Park, E. and del Pobil, A.P., 2022. Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism, 24(1), pp.109-126.
[4] Smetanin, S. and Komarov, M., 2019, July. Sentiment analysis of product reviews in Russian using convolutional neural networks. In 2019 IEEE 21st conference on business informatics (CBI) (Vol. 1, pp. 482-486). IEEE.
[5] Noor, A. and Islam, M., 2019, July. Sentiment Analysis for Women's E-commerce Reviews using Machine Learning Algorithms. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE.
[6] Wassan, S., Chen, X., Shen, T., Waqar, M. and Jhanjhi, N.Z., 2021. Amazon product sentiment analysis using machine learning techniques. Revista Argentina de Clínica Psicológica, 30(1), p.695.
[7] Noori, B., 2021. Classification of customer reviews using machine learning algorithms. Applied Artificial Intelligence, 35(8), pp.567-588.
[8] Chang, Y.C., Ku, C.H. and Le Nguyen, D.D., 2022. Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry. Information & Management, 59(2), p.103587.
[9] Liu, Y., Lu, J., Yang, J. and Mao, F., 2020. Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax. Mathematical Biosciences and Engineering, 17(6), pp.7819-7837.
[10] Norinder, U. and Norinder, P., 2022. Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction. Journal of Management Analytics, 9(1), pp.1-16.
[11] Alzahrani, M.E., Aldhyani, T.H., Alsubari, S.N., Althobaiti, M.M. and Fahad, A., 2022. Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E‐Commerce Product Reviews. Computational Intelligence and Neuroscience, 2022(1), p.3840071.
[12] Rasappan, P., Premkumar, M., Sinha, G. and Chandrasekaran, K., 2024. Transforming sentiment analysis for e-commerce product reviews: Hybrid deep learning model with an innovative term weighting and feature selection. Information Processing & Management, 61(3), p.103654.
[13] Zhao, Z., Zhang, N., Xiong, J., Feng, M., Jiang, C. and Wang, X., 2024. Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN. Journal of Theory and Practice of Engineering Science, 4(02), pp.38-44.
[14] Ahmed, S.R., ALhashmi, L.Q., Hussein, A.J., Radhi, A.D., Radhi, A.A., Ahmed, M.R., Hussain, A.S.T. and Tawfeq, J.F., 2024, May. Deep Learning for Customer Relationship Management in E-commerce. In 2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE.
[15] Li, D. and Esquivel, J.A., 2024. Accuracy-enhanced E-commerce recommendation based on deep learning and locality-sensitive hashing. Wireless Networks, pp.1-16.
[16] Mamta, K. and Sangwan, S., 2024. AaPiDL: an ensemble deep learning-based predictive framework for analyzing customer behaviour and enhancing sales in e-commerce systems. International Journal of Information Technology, 16(5), pp.3019-3025.
[17] Huang, W., Lin, M. and Wang, Y., 2022. Sentiment analysis of Chinese e-commerce product reviews using ERNIE word embedding and attention mechanism. Applied Sciences, 12(14), p.7182.
[18] Ali, H., Hashmi, E., Yayilgan Yildirim, S. and Shaikh, S., 2024. Analyzing amazon products sentiment: a comparative study of machine and deep learning, and transformer-based techniques. Electronics, 13(7), p.1305.
[19] Greeshma, M. and Simon, P., 2024. Bidirectional Gated Recurrent Unit with Glove Embedding and Attention Mechanism for Movie Review Classification. Procedia Computer Science, 233, pp.528-536.
[20] Lui, C.F., Liu, Y. and Xie, M., 2022. A supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling. IEEE Transactions on Instrumentation and Measurement, 71, pp.1-13.
[21] Lei, X., 2024. A Photovoltaic Prediction Model with Integrated Attention Mechanism. Mathematics, 12(13), p.2103.
[22] Ragab M., 2022. Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction. Computers, Materials & Continua. 72(2), pp. 4143-4155.
[23] Mohamed, B., Mohaisen, L. and Amin, M., 2023. Binary Archimedes Optimization Algorithm for Computing Dominant Metric Dimension Problem. Intelligent Automation & Soft Computing, 38(1).
[24] Algarni, A., Ragab M., Alamri W., Mostafa S.M., 2022. Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique. Computer Systems Science And Engineering. 42(1), pp. 303-18.
[25] https://www.kaggle.com/datasets/arhamrumi/amazon-product-reviews
[26] Jiang, T., Sun, W. and Wang, M., 2023. MSGAT-based sentiment analysis for e-commerce. Information, 14(7), p.416.
[27] Basheri M., Ragab, M. , 2024. Robust Financial Market Share Prediction using Intuitionistic Possibility Fermatean Neutrosophic Soft Set. International Journal of Neutrosophic Science. 24 (3) pp. 115-126.
