A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 6 | Issue - 1 | march 2024
Published
21 March, 2024
The Bidirectional Encoder Representations from Transformers (BERT) model is used in this work to analyse sentiment on Twitter data. A Kaggle dataset of manually annotated and anonymized COVID-19-related tweets was used to refine the model. Location, tweet date, original tweet content, and sentiment labels are all included in the dataset. When compared to the Multinomial Naive Bayes (MNB) baseline, BERT's performance was assessed, and it achieved an overall accuracy of 87% on the test set. The results indicated that for negative feelings, the accuracy was 0.93, the recall was 0.84, and the F1-score was 0.88; for neutral sentiments, the precision was 0.86, the recall was 0.78, and the F1-score was 0.82; and for positive sentiments, the precision was 0.82, the recall was 0.94, and the F1-score was 0.88. The model's proficiency with the linguistic nuances of Twitter, including slang and sarcasm, was demonstrated. This study also identifies the flaws of BERT and makes recommendations for future research paths, such as the integration of external knowledge and alternative designs.
KeywordsBERT Natural Language Processing Machine Learning Sentiment analysis Multinomial Naive Bayes
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