Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method

Pambudi, Agung; Suprapto, Suprapto

doi:10.22146/ijccs.61627

Open access Jurnal SINTA 2

Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 1 (2021) · pp. 21–30

DOI: 10.22146/ijccs.61627
ISSN: 2460-7258, 1978-1520
Published: 2021-01-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Based on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performance is assessed through students' learning evaluation system by giving comments to the instructions. These comments contain positive and negative sentiments that can be reviewed by conducting sentiment analysis. Research related to sentiment analysis in recent years has been widely done, but researchers rarely pay attention to the effect of sentence length from the dataset on the method's performance. This study tried to analyze sentiment related to sentence length effect on ICT training student comments using Support Vector Machine and Convolutional Neural Network methods. This study concluded that the sentence length on the dataset would affect the SVM and CNN methods' performance when combined with Word2vec. While the SVM+TFIDF method performance is not affected by sentence length, this method has the fastest process time than other methods. The CNN+Word2vec method produced the best performance in this study with a value of 0.94% accuracy, 0.95% precision, 0.96% recall, and 0.95% f1-score.

Keywords

Collaboration network of the authors

16 authors · 24 co-authorship links · 8 across different organisations

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Universitas Gadjah Mada
University of Brawijaya
National Research and Innovation Agency
Sumatera Institute of Technology
Universitas Negeri Surabaya
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

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Co-author	Organisation	Joint articles
Arief Ichwani	Sumatera Institute of Technology	1
Windyaning Ustyannie	Universitas Gadjah Mada	1
Devi Valentino Waas	Universitas Gadjah Mada	1
Muhammad Faqih Dzulqarnain	Universitas Gadjah Mada	1
Faizal Makhrus	Universitas Gadjah Mada	1
Wahyono Wahyono	Universitas Gadjah Mada	1
Yaqutina Marjani Santosa	Universitas Gadjah Mada	1
Nasution Nasution	Universitas Gadjah Mada	1
Yusi Tyroni Mursityo	University of Brawijaya	1
Muhammad Ghiffari	University of Brawijaya	1
Yurizka Melia Sari	Universitas Negeri Surabaya	1
Janu Arlinwibowo	National Research and Innovation Agency	1
Gupita Nadindra Fatima	Affiliation unknown	1
Dwi Purwoko	National Research and Innovation Agency	1

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.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Effect of Sentence Length in Sentiment Analysis  Using Support Vector Machine and  Convolutional Neural Network Method</dc:title>
	<dc:creator>Pambudi, Agung</dc:creator>
	<dc:creator>Suprapto, Suprapto</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment analysis; SVM; CNN</dc:subject>
	<dc:description xml:lang="en-US">Based on Article 10 paragraph 1 of Law No. 14 of 2005, a teacher must have four competencies: pedagogical, personality, social, and professional. ICT training at Sunan Kalijaga State Islamic University involves instructors as educators who must have such competencies. An instructor's performance is assessed through students' learning evaluation system by giving comments to the instructions. These comments contain positive and negative sentiments that can be reviewed by conducting sentiment analysis. Research related to sentiment analysis in recent years has been widely done, but researchers rarely pay attention to the effect of sentence length from the dataset on the method's performance. This study tried to analyze sentiment related to sentence length effect on ICT training student comments using Support Vector Machine and Convolutional Neural Network methods. This study concluded that the sentence length on the dataset would affect the SVM and CNN methods' performance when combined with Word2vec. While the SVM+TFIDF method performance is not affected by sentence length, this method has the fastest process time than other methods. The CNN+Word2vec method produced the best performance in this study with a value of 0.94% accuracy, 0.95% precision, 0.96% recall, and 0.95% f1-score.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
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	<dc:date>2021-01-31</dc:date>
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	<dc:identifier>10.22146/ijccs.61627</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 1 (2021): January; 21-30</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/61627/30555</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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