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Open access Jurnal SINTA 2

Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 4 (2022) · pp. 367–378

DOI
10.22146/ijccs.72981
ISSN
2460-7258, 1978-1520
Published
2022-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Aspect detection systems for online reviews, especially based on unsupervised models, are considered better strategically to process online reviews, generally a very large collection of unstructured data. Aspect embedding-based deep learning models are designed for this problem however they still rely on redundant word embedding and they are sensitive to initialization which may have a significant impact on model performance. In this research, a pruning approach is used to reduce the redundancy of deep learning model connections and is expected to produce a model with similar or better performance. This research includes several experiments and comparisons of the results of pruning the model network weights based on the general neural network pruning strategy and the lottery ticket hypothesis. The result of this research is that pruning of the unsupervised aspect detection model, in general, can produce smaller submodels with similar performance even with a significant amount of weights pruned. Our sparse model with 80% of its total weight pruned has a similar performance to the original model. Our current pruning implementation, however, has not been able to produce sparse models with better performance.

Keywords

Collaboration network of the authors

15 authors · 25 co-authorship links · 3 across different organisations

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

  • Bandung Institute of Technology
  • Indonesia University of Education
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dwi Hendratmo WidyantoroBandung Institute of Technology4
Bambang Riyanto TrilaksonoBandung Institute of Technology3
E. Aminudin AzizIndonesia University of Education3
Jan Wira Gotama PutraBandung Institute of Technology1
Ghoziyah Haitan RachmanBandung Institute of Technology1
Carmadi MachbubBandung Institute of Technology1
Wahyudi WahyudiBandung Institute of Technology1
Ary Setijadi PrihatmantoBandung Institute of Technology1
Benhard SitohangBandung Institute of Technology1
Gusti Ayu Putri SaptawatiBandung Institute of Technology1
Nasy`an Taufiq Al GhifariBandung Institute of Technology1
Richard ReinhartBandung Institute of Technology1
Bariza HaqiBandung Institute of Technology1

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">Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding</dc:title>
	<dc:creator>Maulana, Muhammad Haris</dc:creator>
	<dc:creator>Khodra, Masayu Leylia</dc:creator>
	<dc:subject xml:lang="en-US">Natural language processing, Information extraction</dc:subject>
	<dc:subject xml:lang="en-US">aspect embedding; unsupervised aspect detection; pruning; lottery ticket hypothesis</dc:subject>
	<dc:description xml:lang="en-US"> Aspect detection systems for online reviews, especially based on unsupervised models, are considered better strategically to process online reviews, generally a very large collection of unstructured data.  Aspect embedding-based deep learning models are designed for this problem however they still rely on redundant word embedding and they are sensitive to initialization which may have a significant impact on model performance. In this research, a pruning approach is used to reduce the redundancy of deep learning model connections and is expected to produce a model with similar or better performance. This research includes several experiments and comparisons of the results of pruning the model network weights based on the general neural network pruning strategy and the lottery ticket hypothesis. The result of this research is that pruning of the unsupervised aspect detection model, in general, can produce smaller submodels with similar performance even with a significant amount of weights pruned. Our sparse model with 80% of its total weight pruned has a similar performance to the original model. Our current pruning implementation, however, has not been able to produce sparse models with better performance. </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Institut Teknologi Bandung</dc:contributor>
	<dc:date>2022-10-31</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:identifier>https://journal.ugm.ac.id/ijccs/article/view/72981</dc:identifier>
	<dc:identifier>10.22146/ijccs.72981</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 4 (2022): October; 367-378</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/72981/34985</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 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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