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Open access Jurnal SINTA 2

Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 2 (2020) · pp. 123–134

DOI
10.22146/ijccs.51646
ISSN
2460-7258, 1978-1520
Published
2020-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%.

Keywords

Collaboration network of the authors

29 authors · 55 co-authorship links · 17 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Dian Nuswantoro
  • Udayana University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus SihabuddinUniversitas Gadjah Mada2
Yunita SariUniversitas Gadjah Mada2
Ghulam ImanuddinUniversitas Gadjah Mada1
Suci Karunia PrilistyaUniversitas Gadjah Mada1
Azhari SnUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Edi WinarkoUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Auliya Rahman IsnainUniversitas Teknokrat Indonesia1
Annisaa UtamiUniversitas Gadjah Mada1
Riszki Wijayatun PratiwiUniversitas Gadjah Mada1
Khabib MustofaUniversitas Gadjah Mada1
Amalia Nur AnggraeniUniversitas Gadjah Mada1
Huda MustakimUniversitas Gadjah Mada1
Sensa Gudya Sauma SyahraUniversitas Gadjah Mada1
Dika Permana PutraUniversitas Gadjah Mada1
Nur FadilahUniversitas Gadjah Mada1
Muthia Farah HanifaSyarif Hidayatullah State Islamic University Jakarta1
Anugrah Tri RamadhanUniversitas Dian Nuswantoro1
Ni’Matul HusnaUniversitas Gadjah Mada1
Nabila Apriliana WidiyonoUniversitas Gadjah Mada1
Rhamdan Syahrul MubarakUniversitas Gadjah Mada1
Adisti Anjani PutriUdayana University1
Fadila Shely AmaliaUniversitas Gadjah Mada1

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">Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network</dc:title>
	<dc:creator>Ari Bangsa, MHD Theo</dc:creator>
	<dc:creator>Priyanta, Sigit</dc:creator>
	<dc:creator>Suyanto, Yohanes</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">aspect-based sentiment analysis; convolutional neural network; online store</dc:subject>
	<dc:description xml:lang="en-US">Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%.</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>2020-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/51646</dc:identifier>
	<dc:identifier>10.22146/ijccs.51646</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 2 (2020): April; 123-134</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/51646/27870</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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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