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Performance analysis of sentiments in Twitter dataset using SVM models

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 3 (2021)

DOI
10.11591/ijece.v11i3.pp2275-2284
ISSN
2722-2578, 2088-8708
Published
2021-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.

Collaboration network of the authors

27 authors · 75 co-authorship links · 39 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Vignan's Foundation for Science, Technology & Research
  • Soonchunhyang University
  • Hindustan Institute of Technology and Science
  • Al-Mustaqbal University
  • Higher Colleges of Technology
  • Beirut Arab University
  • University of Technology Malaysia
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Firoz KhanHigher Colleges of Technology11
R. Lakshmana KumarHindustan Institute of Technology and Science5
A. SaravananAffiliation unknown2
S. Sathya BamaAffiliation unknown2
Jinesh AhamedHigher Colleges of Technology2
P. ChandanaVignan's Foundation for Science, Technology & Research2
G S Pradeep GhantasalaAffiliation unknown2
J. R. V. JenyVignan's Foundation for Science, Technology & Research2
Kaushik SekaranVignan's Foundation for Science, Technology & Research2
N. DeepikaVignan's Foundation for Science, Technology & Research2
Saif O. HusainIraqi University2
Alyaa Mohammed JawadAl-Mustaqbal University2
Ahlam R. KhekanUniversity of Information Technology and Communications2
Mohammed Zuhair Al-TaieUniversity of Technology Malaysia1
Joel Pinho LucasAffiliation unknown1
Venkatesan RajinikanthSt. Joseph’s College of Engineering1
Jamin KooSoonchunhyang University1
Byeong-Gwon KangSoonchunhyang University1
Ganesh ChandrasekaranSri Eshwar College of Engineering1
M. Kalpana ChowdaryAffiliation unknown1
Jyothi Chinna BabuAffiliation unknown1
Ajmeera KiranAffiliation unknown1
Kotthuru Anil KumarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Performance analysis of sentiments in Twitter dataset using SVM models</dc:title><dc:creator>Ramasamy, Lakshmana Kumar</dc:creator><dc:creator>Kadry, Seifedine</dc:creator><dc:creator>Nam, Yunyoung</dc:creator><dc:creator>Meqdad, Maytham N.</dc:creator><dc:description>&lt;jats:p&gt;Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i3.pp2275-2284</dc:identifier><dc:identifier>10.11591/ijece.v11i3.pp2275-2284</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 3 (2021); 2275</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/21825/14776</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>