Recommendation System in E-Commerce Using Machine Learning Methods and Emotional Analysis-Reference-Cited by-同舟云学术

Recommendation System in E-Commerce Using Machine Learning Methods and Emotional Analysis

Published:2024-08-19 Issue: Volume: Page:626-629
ISSN:2581-9429
Container-title:International Journal of Advanced Research in Science, Communication and Technology
language:en
Short-container-title:IJARSCT

Author:

Maria Momin ¹,Zeenat Qureshi ¹,Ayesha Sayed ¹,Sana Shaikh ¹,Prof Dr. Ashfaq Shaikh ¹

Affiliation:

1. M.H. Saboo Siddik College of Engineering, Mumbai, Maharashtra, India

Abstract

Amazon is an e-commerce website which is beneficial for commercial use. It makes use of commercial website easy, it is accompanied with ratings and review which makes it even more simpler to choose the product. There are all sort of rating, i.e positive, negative, neutral according the product rating is done, the procedure of review and rating are considered to be one of the most important part to reach customer expectations. It is important to analyze the product and recommend accordingly to satisfy user. Decision Trees, Random Forest are implemented to perform sentiment analysis on products based on the reviews given by the customer. It was seen that Random forest gave a higher accuracy of 88.87 percent in detecting the same. Amazon has changed and upgraded over years, It makes selling and purchasing of products online in an efficient manner. It secures the customer details, It also provides fast home delivery and good packing. We are using ensemble classifiers to provide accurate recommendations

Publisher

Naksh Solutions

Reference9 articles.

1. [1] Ahlgren, Oskar (2016). “Research on Sentiment Analysis: The First Decade”. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

2. [2] Dang, Nhan Cach, Marıa N Moreno-Garcıa and Fernando De la Prieta (2020). “Sentiment Analysis Based on Deep Learning: A Comparative Study”. In: Electronics 9.3.

3. [3] Gupta, Pankaj, Ritu Tiwari and Nirmal Robert (2016). “Sentiment analysis and Text Summarization of Online Reviews: A Survey”. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 0241–0245.

4. [4] F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh,“Recommendation systems: Principles, methods and evaluation,” Egypt. Informatics J., vol. 16, no. 3, pp.261–273, 2015.

5. [5] Jagdale RS, Shirsat VS, Deshmukh SN. Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft Computing, Springer, Singapore, pp. 639–647, 2019.