i-manager Publications

Ensemble Dynamic Machine Learning Algorithm (EDMLA) for E-Commerce Sentiment Product Recommendation System with the Integration of AACSD-An Empirical Study

V. Sujay*, M. Babu Reddy**

* Department of Computer Science and Engineering, Krishna University, Machilipatnam, Andhra Pradesh, India.

** University College of Engineering, Krishna University, Machilipatnam, Andhra Pradesh, India.

Periodicity:January - June'2023
DOI : https://doi.org/10.26634/jaim.1.1.19245

Abstract

In this paper, an attempt has been made to investigate the benefits of the Amalgamate Architecture Centric Software Development (AACSD) method through an experimental setup using Machine Learning techniques on an E-Commerce product recommender system. The system recommends products based on authorized user reviews. As part of this research, an Ensemble Dynamic Machine Learning Algorithm (EDMLA) was designed and developed with the integration of AACSD to improve performance quality. Performance was evaluated based on parameters such as sensitivity, specificity, and accuracy.

Keywords

Sentiment Analysis, AACSD, Machine Learning, Deep Learning, EDMLA.

How to Cite this Article?

Sujay, V., and Reddy, M. B. (2023). Ensemble Dynamic Machine Learning Algorithm (EDMLA) for E-Commerce Sentiment Product Recommendation System with the Integration of AACSD-An Empirical Study. i-manager’s Journal on Artificial Intelligence & Machine Learning, 1(1), 12-19. https://doi.org/10.26634/jaim.1.1.19245

References

[1]. Alpaydin, E. (2004). Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). The MIT Press Cambridge.

[2]. Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3-4), 197-387.

[3]. García-Díaz, V., Espada, J. P., Crespo, R. G., GBustelo, B. C. P., & Lovelle, J. M. C. (2018). An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis. Applied Soft Computing, 67, 822-833.

[4]. Jotheeswaran, J., & Koteeswaran, S. (2015). Decision tree based feature selection and multilayer perceptron for sentiment analysis. Journal of Engineering and Applied Sciences, 10(14), 5883-5894.

[5]. Lafferty, J., McCallum, A., & Pereira, F. C. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning, (pp. 282–289).

[6]. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10).

[7]. Ramadhan, W. P., Novianty, S. A., & Setianingsih, S. C. (2017, September). Sentiment analysis using multinomial logistic regression. In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC) (pp. 46-49). IEEE.

[8]. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126.

[9]. Xia, R., Xu, F., Zong, C., Li, Q., Qi, Y., & Li, T. (2015). Dual sentiment analysis: Considering two sides of one review. IEEE Transactions on Knowledge and Data Engineering, 27(8), 2120-2133.

[10]. Zainuddin, N., & Selamat, A. (2014, September). Sentiment analysis using support vector machine. In 2014 International Conference on Computer, Communications, and Control Technology (I4CT) (pp. 333-337). IEEE.

[11]. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.

[12]. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1-38.

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	40	40	300
Online	40	40	300
Pdf & Online	40	40	300

Ensemble Dynamic Machine Learning Algorithm (EDMLA) for E-Commerce Sentiment Product Recommendation System with the Integration of AACSD-An Empirical Study

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: