Sentiment Analysis of E-Commerce Reviews using Machine Learning

Year : 2024 | Volume :11 | Issue : 03 | Page : –
By

Riya Kawale,

Namrata Patole,

Sahil Karekar,

Vedant Padhye,

  1. Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
  2. Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
  3. Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
  4. Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India

Abstract

‘]

In e-commerce, sentiment pertains to the emotional responses, opinions, or perceptions that customers have about their online shopping experiences, including factors like product quality, service, and various processes such as ordering, shipping, and customer support. Sentiment analysis, which involves machine learning techniques, plays a crucial role in deciphering these sentiments. By using sentiment analysis, companies can obtain valuable insights from customer feedback from diverse online sources, including social media, surveys, and e-commerce reviews.
The rise of information technology has greatly enhanced convenience, making e-commerce a favored option for purchasing and selling products without the need to visit physical stores. E-commerce encompasses the sale, distribution, and marketing of goods and services, with customer reviews and ratings becoming increasingly important after a purchase. In today’s competitive market, sentiment analysis is widely used to enhance operational efficiency and support strategic decision-making.

Sentiment analysis commonly employs machine learning models such as Support Vector Machine (SVM), Random Forest, and XGBoost. These models are designed to accurately classify and predict customer sentiments based on the collected data. Their performance is usually measured using metrics like Accuracy, F1-Score, Precision, and Recall. Evaluating these metrics helps in identifying the most effective model, providing actionable insights that drive business improvements and growth. Overall, sentiment analysis is an essential tool for understanding customer feedback and achieving success in the e-commerce industry. Using supervised machine learning techniques, models such as Support

Algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost are commonly employed in sentiment analysis. They help accurately categorize and predict customer sentiments based on the collected data. The performance of these models is typically evaluated using metrics like Accuracy, F1-Score, Precision, and Recall. Analyzing these metrics helps identify the best-performing model, providing actionable insights that guide business enhancements and foster growth.

Keywords: Machine Learning, E-commerce, XGBoost, Sentiment analysis, Random Forest.

[This article belongs to Journal of Operating Systems Development & Trends (joosdt)]

How to cite this article:
Riya Kawale, Namrata Patole, Sahil Karekar, Vedant Padhye. Sentiment Analysis of E-Commerce Reviews using Machine Learning. Journal of Operating Systems Development & Trends. 2024; 11(03):-.
How to cite this URL:
Riya Kawale, Namrata Patole, Sahil Karekar, Vedant Padhye. Sentiment Analysis of E-Commerce Reviews using Machine Learning. Journal of Operating Systems Development & Trends. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joosdt/article=2024/view=172103



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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received July 3, 2024
Accepted July 31, 2024
Published September 14, 2024
Views: 39

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