Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 4 , 31 May 2023


Open Access | Article

Sentiment analysis for social media using SVM classifier of machine learning

Qingyu Huang * 1
1 College of Liberal Arts & Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois, 61820, United States

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 4, 86-90
Published 31 May 2023. © 31 May 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Qingyu Huang. Sentiment analysis for social media using SVM classifier of machine learning. ACE (2023) Vol. 4: 86-90. DOI: 10.54254/2755-2721/4/20230354.

Abstract

The community’s perspectives and comments are a valuable resource for businesses and other organizations. In the past, businesses used inefficient procedures. Now that social media is the new trend, it enables an unprecedented level of analysis and evaluation. This enables unprecedented analysis and evaluation of various factors. This enables unprecedented analysis and evaluation of a wide range of topics and components in different contexts and settings. Throughout business history, these strategies have been expected. This field of study is called “sentiment analysis.” SVM was used to analyze sentiment for this research project. One of these duties required an SVM(SVM). Support vector machines, or SVM, is a popular supervised machine learning algorithm for determining text polarity. SVM abbreviates support vector machines. Precision, recall, and F-measure are used to evaluate SVM using two datasets of pre-classified tweets. Tables and graphs are used to communicate research findings. This research classifies tweets about US-Airlines and performs sentiment analysis with an accuracy of 91.8 percent, precision of 91.3 percent, and recall of 82.3 percent, as well as the F1 of 86.9 percent.

Keywords

SVM, Classifier, Sentiment Analysis, Emotions, US-Airlines

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-55-3
ISBN (Online)
978-1-915371-56-0
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/4/20230354
Copyright
31 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated