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Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 2 (2019) · pp. 145–161

DOI
10.5614/itbj.ict.res.appl.2019.13.2.5
ISSN
2338-5499, 2337-5787
Published
2019-09-30
Publisher
DRI - ITB
Language
eng

Abstract

Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers.

Keywords

Collaboration network of the authors

8 authors · 22 co-authorship links

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

  • Kyushu University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Anak Agung Gede DharmaKyushu University1
Hiroyuki KumamotoKyushu University1
Shogo KochiKyushu University1
Natsuki KudoKyushu University1
Wei GuoweiKyushu University1
Shu-Chuan ChiuKyushu University1

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">Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products</dc:title>
	<dc:creator>Gani, Hamdan</dc:creator>
	<dc:creator>Tomimatsu, Kiyoshi</dc:creator>
	<dc:subject xml:lang="en-US">advice</dc:subject>
	<dc:subject xml:lang="en-US">customer review</dc:subject>
	<dc:subject xml:lang="en-US">emotional experience</dc:subject>
	<dc:subject xml:lang="en-US">linguistic description of evaluation</dc:subject>
	<dc:subject xml:lang="en-US">natural language processing</dc:subject>
	<dc:description xml:lang="en-US">Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/8168</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.2.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 2 (2019); 145-161</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2019.13.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/8168/4338</dc:relation>
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