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Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 12 No. 1 (2018) · pp. 1–10

DOI
10.22146/ijccs.19237
ISSN
2460-7258, 1978-1520
Published
2018-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Movie has unique characteristics. When someone writes an opinions about a movie, not only the story in the movie itself is written, but also the people involved in the movie are also written. Opinion ordinary movie written in social media primarily twitter.To get a tendency of opinion on the movie, whether opinion is likely positive, negative or neutral, it takes a sentiment analysis. This study aims to classify the sentiment is positive, negative and neutral from opinions Indonesian language movie and look for the accuracy, precission, recall and f-meausre of the method used is Dynamic Convolutional Neural Network. The test results on a system that is built to show that Dynamic Convolutional Neural Network algorithm provides accuracy results better than Naive Bayes method, the value of accuracy of 80,99%, the value of precission 81,00%, recall 81,00%, f-measure 79,00% while the value of the resulting accuracy Naive Bayes amounted to 76,21%, precission 78,00%, recall 76,00%, f-measure 75,00%.

Keywords

Collaboration network of the authors

32 authors · 38 co-authorship links · 12 across different organisations

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

  • Universitas Gadjah Mada
  • Politeknik Negeri Bengkalis
  • Indonesia University of Education
  • Universitas Airlangga
  • Instituto Profissional de Canossa - IPDC
  • Universitas Halu Oleo
  • Institut Teknologi Telkom Purwokerto
  • 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
Sandi Fajar RodiyansyahIndonesia University of Education2
Mardi Siswo UtomoUniversitas Gadjah Mada1
Badrus ZamanUniversitas Airlangga1
Agustinus JacobusUniversitas Gadjah Mada1
Sulistyowati SulistyowatiAffiliation unknown1
Noviah Dwi PutrantiUniversitas Gadjah Mada1
Ambarwati AmbarwatiInstituto Profissional de Canossa - IPDC1
Agus Sasmito AriwibowoAffiliation unknown1
Eka KaryawatiUniversitas Gadjah Mada1
Robynson AmsekeAffiliation unknown1
Natalis RansiUniversitas Halu Oleo1
Sumarni AdiUniversitas Gadjah Mada1
Lutfi Budi IlmawanUniversitas Gadjah Mada1
Wayan Gede Suka ParwitaUniversitas Gadjah Mada1
Fadhila Tangguh AdmojoUniversitas Gadjah Mada1
Siti KhomsahInstitut Teknologi Telkom Purwokerto1
Doni SetyawanUniversitas Widya Dharma Klaten1
Ira ZulfaUniversitas Gadjah Mada1
Rakhmad IkhsanudinUniversitas Gadjah Mada1
Junanda PatihullahUniversitas Gadjah Mada1
Wafda RifaiUniversitas Gadjah Mada1
Sigit PriyantaUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Putri Rizki AmaliaUniversitas Gadjah Mada1
Angel Berta Desi SuryantiUniversitas Gadjah Mada1
Fahmi Reza PrasastioUniversitas Gadjah Mada1
Depandi EndaInstitut Teknologi Sepuluh Nopember1
Agus TedyyanaPoliteknik Negeri Bengkalis1
Muhammad Rizki FauziAffiliation unknown1
Elgamar SyamUniversitas Islam Kuantan Singingi1

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">Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm</dc:title>
	<dc:creator>Ratnawati, Fajar</dc:creator>
	<dc:creator>Winarko, Edi</dc:creator>
	<dc:subject xml:lang="en-US">computer science</dc:subject>
	<dc:subject xml:lang="en-US">sentiment analysis; opinion movies; twitter; Dynamic Convolutional Neural Network</dc:subject>
	<dc:description xml:lang="en-US">Movie has unique characteristics. When someone writes an opinions about a movie, not only the story in the movie itself is written, but also the people involved in the movie are also written. Opinion ordinary movie written in social media primarily  twitter.To get a tendency of opinion on the movie, whether opinion is likely  positive, negative or neutral, it takes a sentiment analysis. This study aims to classify the sentiment is positive, negative and neutral from opinions Indonesian language movie and look for the accuracy, precission, recall and f-meausre of the method used is Dynamic Convolutional Neural Network. The test results on a system that is built to show that Dynamic Convolutional Neural Network algorithm provides accuracy results better than Naive Bayes method, the value of accuracy of 80,99%, the value of precission 81,00%, recall 81,00%, f-measure 79,00%   while the value of the resulting accuracy Naive Bayes amounted to 76,21%, precission 78,00%, recall 76,00%, f-measure 75,00%.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2018-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/19237</dc:identifier>
	<dc:identifier>10.22146/ijccs.19237</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 12, No 1 (2018): January; 1-10</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/19237/19813</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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