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 - 5 | Issue - 2 | june 2023
Published
27 May, 2023
There are so many social networking sites available. Tweets have evolved into a crucial tool for gathering people's thoughts, ideas, behaviours and sentiments surrounding particular entities. One of the most intriguing subjects in this context is analyzing the sentiment of tweets using natural language processing (NLP). Although several methods have been created, the accuracy and effectiveness of those methods for sentiment analysis are yet to be improved. This paper proposes an innovative strategy that takes advantage of machine learning and lexical dictionaries. Tweets are classified using a stacked ensemble model that has Naive Bayes as a base classifier and the Logistic Regression as a meta classifier model. The performance of the proposed method is compared with common machine learning models such as Naïve Bayes and Logistic Regression using the sentiment140 dataset, experiments were carried out and their accuracy was determined. The results of the experiment endorse the proposed methodology. exhibits better outcomes of attaining accuracy score of 86%.
Keywordssentiment analysis ensemble model logistic regression natural language processing naive bayes machine learning
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