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
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Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
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Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
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Augmented Reality in Education
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A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
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Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4
IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3
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
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
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
A Smart Climatic Control Strategy for Optimizing Vegetable Crop Cultivation in Greenhouse using FBANN
Volume-3 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Volume - 4 | Issue - 1 | march 2022
Published
26 May, 2022
For assessing customer sentiment in Amazon product reviews, this article compares two machine learning algorithms and a deep learning method, BERT (Bidirectional Encoder Representations from Transformer). Machine learning is the most practical approach in the current era of artificial intelligence for training a neural network to handle the majority of real-world issues. In this paper, the real-world scenario of sentiment analysis is considered, ideally the classification problem. Firstly, the data is provided into a model, which evaluates the feature that uses the Term Frequency (TF) and Inverse Document Frequency (IDF) pre-processing methods. Secondly, the algorithms, Naive Bayes classifier and Support Vector Machine are used to analyze the sentiment of the consumer comments and compute metrics like F1 score. Finally, the input data is fed for BERT to process and compute the F1 score. To summarize, this study is to provide a detailed comparative analysis of machine learning techniques and deep learning algorithms.
KeywordsSentiment analysis Naïve Bayes classifier SVM BERT
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