Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
Volume-2 | Issue-4
Augmented Reality in Education
Volume-2 | Issue-4
A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
Volume-2 | Issue-2
IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3
Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4
GUI based Industrial Monitoring and Control System
Volume-3 | Issue-2
AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3
Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3
Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 5 | Issue - 1 | march 2023
Published
11 May, 2023
Digital reviews now have a significant impact on how consumers communicate globally and the effect of review on online purchases. Sentiment analysis is one of most common field for analysing and extracting information from data of text format from multiple platforms like Facebook, Amazon, Twitter, and others. This research aims to discuss and analyse the sentiments stated in Amazon product reviews using Support Vector Machine (SVM), Naive Bayes, Random Forest, and Decision Tree algorithms. The machine learning algorithms classify reviews as positive or negative. The performance of the algorithms is evaluated in terms of metrics such as accuracy, precision, recall and F score. As per the validated results, SVM performs better on accuracy rate compared to the other algorithms.
KeywordsMachine learning Product review Support Vector Machine Performance Metrics
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