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Aspect based Sentiment Analysis of Employee's Review Experience

Journal of Information Systems Engineering and Business Intelligence · Vol. 6 No. 1 (2020) · pp. 79–88

DOI
10.20473/jisebi.6.1.79-88
ISSN
2443-2555, 2598-6333
Published
2020-04-27
Publisher
Universitas Airlangga
Language
eng

Abstract

Background: Employees of technology companies evaluate their experience through online reviews. Online reviews of companies from employees or former employees help job seeker to find out the weaknesses and strengths of the companies. The reviews can be used as an evaluation tool for each technology company to understand their employee's perceptions. However, most information on online reviews is not well responded since some of the detailed information of the company is missing. Objective: This study aims to generate an Aspect-based Sentiment Analysis using user review data. The review data were then extracted and classified into five aspects: work balance, culture value, career opportunities, company benefit, and management. The output of this study is the aspect score from each company.Methods: This study suggests a method to analyze online reviews from employees in detail, so it can prevent the missing of specific information. The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis.Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception.Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses. This study could possibly also provide ratings to the companies based on the aspects that have been determined.

Collaboration network of the authors

10 authors · 22 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.

  • Asian Institute of Technology
  • Universitas Nahdlatul Ulama Surabaya
  • Université de Lorraine
  • Universitas Airlangga
  • University of Brawijaya
  • State University of Malang
  • University of Pembangunan Nasional Veteran Jawa Timur
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Aji Akbar FirdausUniversitas Airlangga2
Matthew N. DaileyAsian Institute of Technology1
Yuita Arum SariUniversity of Brawijaya1
Syamsul BachriState University of Malang1
Mochammad Nafkhan AlzamzamiAffiliation unknown1
Teguh HerlambangUniversitas Nahdlatul Ulama Surabaya1
Vaizal Asy’ariUniversity of Pembangunan Nasional Veteran Jawa Timur1
Ragil Puji RahayuUniversitas Nahdlatul Ulama Surabaya1

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://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Aspect based Sentiment Analysis of Employee's Review Experience</dc:title>
	<dc:creator>Dina, Nasa Zata</dc:creator>
	<dc:creator>Juniarta, Nyoman</dc:creator>
	<dc:description xml:lang="en-US">Background: Employees of technology companies evaluate their experience through online reviews. Online reviews of companies from employees or former employees help job seeker to find out the weaknesses and strengths of the companies.  The reviews can be used as an evaluation tool for each technology company to understand their employee's perceptions. However, most information on online reviews is not well responded since some of the detailed information of the company is missing. Objective: This study aims to generate an Aspect-based Sentiment Analysis using user review data. The review data were then extracted and classified into five aspects: work balance, culture value, career opportunities, company benefit, and management. The output of this study is the aspect score from each company.Methods: This study suggests a method to analyze online reviews from employees in detail, so it can prevent the missing of specific information. The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis.Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception.Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses. This study could possibly also provide ratings to the companies based on the aspects that have been determined.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2020-04-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/18104</dc:identifier>
	<dc:identifier>10.20473/jisebi.6.1.79-88</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 6 No. 1 (2020): April; 79-88</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/18104/10201</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 The Authors. Published by Universitas Airlangga</dc:rights>
</oai_dc:dc>