Binary Sentiment Analysis And Sentiment Marketing Strategy Optimization Of E-Commerce Platform User Comments Based On Deep Learning Algorithm

Article Sidebar

Published: Apr 30, 2025
Keywords:
Emotional Marketing E-commerce Hidden Markov Model bi—gram Deep learning Optimization

Main Article Content

Jin Gao
Department of Global Food Service Management, Woosuk University, 55338, Jeollabukdo, Korea
Xiang Li*
Department of Global Food Service Management, Woosuk University, 55338, Jeollabukdo, Korea
Wenbo Ma
College of Teacher Education, Jining University, Qufu 273155, Shandong, China

Abstract

Binary sentiment analysis plays a crucial role in e-commerce marketing strategy optimization by classifying user comments into positive or negative sentiments, allowing businesses to analyze customer feedback, predict user behavior, and enhance personalized marketing efforts. This paper explores the application of the bi-gram Hidden Markov Optimization (bi-gram HMMO) model in sentiment analysis within e-commerce platforms in China. Leveraging natural language processing techniques, the bi-gram HMMO model captures intricate dependencies between consecutive words to discern user sentiment from textual data such as reviews and comments. Through a systematic analysis of user interactions, the model accurately identifies and categorizes emotional states, providing valuable insights into customer satisfaction levels and areas for improvement. BERT + JOA achieved a 95.6% accuracy and a 95.4% F1-score, outperforming traditional models like Bi-gram HMM (78.5%) and CNN + JOA (91.5%). Sentiment-aware marketing strategies lead to higher customer engagement, conversion rates, and revenue growth. After implementing sentiment-optimized strategies, customer conversion increased by 73.3% (from 4.5% to 7.8%), while customer retention improved by 24.9% (from 55.2% to 68.9%). Click-through rates (CTR) doubled (+103.1%), and sentiment-based product recommendations saw a 136.6% increase in engagement, indicating that customers respond better to personalized and emotionally intelligent marketing campaigns. Additionally, monthly revenue growth surged by 127.0%, while the average order value (AOV) increased by 33.4% (from $52.3 to $69.8), demonstrating that sentiment-aware marketing strategies directly influence e-commerce profitability. The findings reveal a prevalence of positive sentiment in user expressions, particularly towards product quality, delivery speed, and customer service, with probability scores ranging from 0.75 to 0.90. Conversely, instances of negative sentiment are associated with product defects or damaged items, yielding lower probability scores of 0.60 to 0.80.

Article Details

Issue
Vol. 167 No. A2 (S) (2025): Technologies and Their Effects on Real-Time Social Development
Section
Articles

References

Alzahrani, M. E., Aldhyani, T. H., Alsubari, S. N., Althobaiti, M. M., & Fahad, A. (2022). Developing an intelligent system with deep learning algorithms for sentiment analysis of e-commerce product reviews. Computational Intelligence and Neuroscience, 2022.

Rasappan, P., Premkumar, M., Sinha, G., & 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), 103654.

Loukili, M., Messaoudi, F., & El Ghazi, M. (2023). Sentiment Analysis of Product Reviews for E-Commerce Recommendation based on Machine Learning. International Journal of Advances in Soft Computing & Its Applications, 15(1).

Bagiyalakshmi N, & A.C.Kothandaraman. (2024). Optimized Deep Learning Model for Sentimental Analysis to Improve Consumer Experience in E-Commerce Websites. Journal of Computer Allied Intelligence, 2(3), 41-54.

Kyaw, K. S., Tepsongkroh, P., Thongkamkaew, C., & Sasha, F. (2023). Business intelligent framework using sentiment analysis for smart digital marketing in the E-commerce era. Asia Social Issues, 16(3), e252965-e252965.

Srinivasa Sai Abhijit Challapalli. (2024). Sentiment Analysis of the Twitter Dataset for the Prediction of Sentiments. Journal of Sensors, IoT & Health Sciences (JSIHS,ISSN: 2584-2560), 2(4), 1-15.

Huang, H., Asemi, A., & Mustafa, M. B. (2023). Sentiment analysis in e-commerce platforms: A review of current techniques and future directions. IEEE Access.

Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE access, 8, 23522-23530.

He, H., Zhou, G., & Zhao, S. (2022). Exploring E-commerce product experience based on fusion sentiment analysis method. IEEE Access, 10, 110248-110260.

Hemalatha P. (2024). Secure Deep Learning Architecture Model for Data Management and Scheduling of E-Commerce Data. Journal of Sensors, IoT & Health Sciences, 2(2), 16-29.

Jabbar, J., Urooj, I., JunSheng, W., & Azeem, N. (2019, May). Real-time sentiment analysis on E-commerce application. In 2019 IEEE 16th international conference on networking, sensing and control (ICNSC) (pp. 391-396). IEEE.

Vatambeti, R., Mantena, S. V., Kiran, K. V. D., Manohar, M., & Manjunath, C. (2024). Twitter sentiment analysis on online food services based on elephant herd optimization with hybrid deep learning technique. Cluster Computing, 27(1), 655-671.

Liu, Y., Lu, J., Yang, J., & Mao, F. (2020). Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax. Mathematical Biosciences and Engineering, 17(6), 7819-7837.

R. Delshi Howsalya Devi, S.Prabu, & N. Legapriyadharshini. (2024). Sentimental Analysis for the Improved User Experience in the E-Commerce Platform with the Fuzzy Model. Journal of Computer Allied Intelligence, 2(3), 28-40.

Zhang, M. (2020, April). E-commerce comment sentiment classification based on deep learning. In 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (pp. 184-187). IEEE.

Zhang, M. (2020, April). E-commerce comment sentiment classification based on deep learning. In 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (pp. 184-187). IEEE.

Arobi, R., Rijon, R. H., Haque, N., Safkat, T. I. M., & Safoan, S. (2022). Sentiment analysis on E-commerce based product reviews using machine learning algorithms (Doctoral dissertation, Brac University).

Lin, X. (2020, April). Sentiment analysis of e-commerce customer reviews based on natural language processing. In Proceedings of the 2020 2nd international conference on big data and artificial intelligence (pp. 32-36).

Cao, J., Li, J., Yin, M., & Wang, Y. (2023). Online reviews sentiment analysis and product feature improvement with deep learning. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(8), 1-17.

Chandio, B., Shaikh, A., Bakhtyar, M., Alrizq, M., Baber, J., Sulaiman, A., ... & Noor, W. (2022). Sentiment analysis of roman Urdu on e-commerce reviews using machine learning. CMES-Comput. Model. Eng. Sci, 131, 1263-1287.

Pons-Muñoz de Morales, S. (2020). Big data and Sentiment Analysis considering reviews from e-commerce platforms to predict consumer behavior.

Zhu, J. J., Chang, Y. C., Ku, C. H., Li, S. Y., & Chen, C. J. (2021). Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning. Journal of Business Research, 129, 860-877.

Chamekh, A., Mahfoudh, M., & Forestier, G. (2022, July). Sentiment analysis based on deep learning in e-commerce. In International Conference on Knowledge Science, Engineering and Management (pp. 498-507). Cham: Springer International Publishing.

Jahan, H., Kowshir Bitto, A., Shohel Arman, M., Mahmud, I., Fahad Hossain, S., Moni Saha, R., & Shohug, M. M. H. (2022). Sentiment analysis of e-commerce consumer based on product delivery time using machine learning. In Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2021 (pp. 649-661). Singapore: Springer Nature Singapore.

Zikang, H., Yong, Y., Guofeng, Y., & Xinyu, Z. (2020, November). Sentiment analysis of agricultural product ecommerce review data based on deep learning. In 2020 International Conference on Internet of Things and Intelligent Applications (ITIA) (pp. 1-7). IEEE.

Cheng, T., & Li, L. (2023). Retracted: Optimization of E‐commerce platform marketing method and comment recognition model based on deep learning and intelligent blockchain. IET Software, 17(4), 797-808.