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Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 1 (2020) · pp. 51–68

DOI
10.5614/itbj.ict.res.appl.2020.14.1.4
ISSN
2338-5499, 2337-5787
Published
2020-07-28
Publisher
DRI - ITB
Language
eng

Abstract

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.

Keywords

Collaboration network of the authors

35 authors · 76 co-authorship links · 16 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
  • National Research and Innovation Agency
  • Indonesia University of Education
  • Telkom University
  • Badan Pusat Statistik
  • Cohere (Canada)
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Masayu Leylia KhodraBandung Institute of Technology4
Bambang Riyanto TrilaksonoBandung Institute of Technology3
E. Aminudin AzizIndonesia University of Education3
Afrida HelenBandung Institute of Technology2
Aswin JuariBandung Institute of Technology1
Novi YuslianiBandung Institute of Technology1
Hari Bagus FirdaudAffiliation unknown1
Zakiy Firdaus AlfikriBandung Institute of Technology1
Untari Novia WisestyTelkom University1
Tati Rajab MengkoBandung Institute of Technology1
Adi PancoroBandung Institute of Technology1
Benhard SitohangBandung Institute of Technology1
Suhono Harso SupangkatBandung Institute of Technology1
Ahmad DahlanBandung Institute of Technology1
Nila NurmalaBadan Pusat Statistik1
Ghoziyah Haitan RachmanBandung Institute of Technology1
Arief Purnama MuharramBandung Institute of Technology1
Ubaidillah Ariq PrathamaBandung Institute of Technology1
Samuel CahyawijayaCohere (Canada)1
Nugraha Priya UtamaBandung Institute of Technology1
Muh. Hafizh IzzaturrahimAffiliation unknown1
Ida Bagus Peling PrayogaAffiliation unknown1
Andika Naufal HilmyAffiliation unknown1
Erwin Budi SetiawanBandung Institute of Technology1
Kridanto SurendroBandung Institute of Technology1
Purnomo Husnul KhotimahNational Research and Innovation Agency1
Andria ArisalNational Research and Innovation Agency1
Andri Fachrur RozieNational Research and Innovation Agency1
Ekasari NugraheniNational Research and Innovation Agency1
Dianadewi RiswantiniNational Research and Innovation Agency1
Wiwin SuwarningsihNational Research and Innovation Agency1
Devi MunandarNational Research and Innovation Agency1

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://journals.itb.ac.id/index.php/jictra/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">Efficient Utilization of Dependency Pattern and  Sequential Covering for Aspect Extraction Rule Learning</dc:title>
	<dc:creator>Ruskanda, Fariska Zakhralativa</dc:creator>
	<dc:creator>Widyantoro, Dwi Hendratmo</dc:creator>
	<dc:creator>Purwarianti, Ayu</dc:creator>
	<dc:subject xml:lang="en-US">aspect-based sentiment analysis</dc:subject>
	<dc:subject xml:lang="en-US">aspect extraction</dc:subject>
	<dc:subject xml:lang="en-US">dependency rule</dc:subject>
	<dc:subject xml:lang="en-US">rule learning</dc:subject>
	<dc:subject xml:lang="en-US">sequential covering</dc:subject>
	<dc:description xml:lang="en-US">The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2020-07-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/12739</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2020.14.1.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 1 (2020); 51-68</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2020.14.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/12739/4859</dc:relation>
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