Paper Key : IRJ************370
Author: D. Vishnu Teja,A. Vishal,K. Vishal,S. Vishnu Teja,Ch. Vishnuvardhan Reddy
Date Published: 15 Nov 2024
Abstract
This project aims to address critical issues in sentiment analysis of Twitter data, focusing on improving accuracy and scalability. In this sentiment analysis, we intend to tackle the challenge of achieving higher classification accuracy by using advanced machine learning algorithms like Naive Bayes, Logistic Regression, and Singular Value Decomposition (SVD). The model is trained and fine-tuned to capture nuanced sentiments, enhancing the overall reliability of sentiment predictions. To enhance scalability, our system is designed to efficiently process large volumes of data and adapt to varying data sizes, ensuring robust performance even under heavy load conditions. By addressing these aspects, our approach seeks to overcome limitations of existing methods and deliver more precise and actionable sentiment insights, ultimately empowering users with valuable information for strategic decision-making. This project ultimately aims to deliver precise and actionable sentiment insights, empowering users with valuable information for strategic decision-making.
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