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A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

Mohemmed Sha*

Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Mohemmed Sha. Email: email

Computer Systems Science and Engineering 2023, 45(1), 857-868. https://doi.org/10.32604/csse.2023.031321

Abstract

The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments. High accuracy of 94.17% is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

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Cite This Article

APA Style
Sha, M. (2023). A cloud based sentiment analysis through logistic regression in AWS platform. Computer Systems Science and Engineering, 45(1), 857-868. https://doi.org/10.32604/csse.2023.031321
Vancouver Style
Sha M. A cloud based sentiment analysis through logistic regression in AWS platform. Comput Syst Sci Eng. 2023;45(1):857-868 https://doi.org/10.32604/csse.2023.031321
IEEE Style
M. Sha, "A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform," Comput. Syst. Sci. Eng., vol. 45, no. 1, pp. 857-868. 2023. https://doi.org/10.32604/csse.2023.031321



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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