Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals

IAES International Journal of Artificial Intelligence · Vol. 12 No. 4 (2023) · pp. 1793–1802

DOI
10.11591/ijai.v12.i4.pp1793-1802
ISSN
2252-8938, 2089-4872
Published
2023-12-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.

Keywords

Collaboration network of the authors

38 authors · 116 co-authorship links · 4 across different organisations

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

  • Universiti Teknologi MARA
  • Indonesia University of Education
  • Bandung Institute of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Raseeda HamzahAffiliation unknown4
Nurqueen Sayang Dinnie WirakarnainAffiliation unknown3
Zainab OthmanAffiliation unknown3
MunirAffiliation unknown2
Herbert SiregarAffiliation unknown1
Ade SobandiAffiliation unknown1
Samialloi NusratulloAffiliation unknown1
Dwi Novia Al HusaeniAffiliation unknown1
Ghina Firdha NabilaAffiliation unknown1
Asep WahyudinAffiliation unknown1
Yudi WibisonoAffiliation unknown1
RasimAffiliation unknown1
Nurul Hidayah Mat ZainUniversiti Teknologi MARA1
Siti Rahayu Abdul AzizUniversiti Teknologi MARA1
Norshahidatul Hasana IshakUniversiti Teknologi MARA1
Heny HendrayatiIndonesia University of Education1
Rosniza RoslanAffiliation unknown1
Amni Munira KhidirAffiliation unknown1
Nugraha NugrahaAffiliation unknown1
Heni MulyaniIndonesia University of Education1
Chew Chiou ShengAffiliation unknown1
Fadilah Ezlina ShahbudinAffiliation unknown1
David Loh Er FuAffiliation unknown1
Sigit NugrohoAffiliation unknown1
Novri AsriAffiliation unknown1
Masnur AliAffiliation unknown1
Eka Fitri Novita SariAffiliation unknown1
Nofi Marlina SiregarAffiliation unknown1
Kuntjoro Adji SidartoBandung Institute of Technology1
Edy SoewonoAffiliation unknown1
Pudjo SukarnoAffiliation unknown1
Tutuka AriadjiAffiliation unknown1
Kenny DavidAffiliation unknown1

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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 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">Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals</dc:title>
	<dc:creator>Abu Samah, Khyrina Airin Fariza</dc:creator>
	<dc:creator>Nor Azharludin, Nur Maisarah</dc:creator>
	<dc:creator>Riza, Lala Septem</dc:creator>
	<dc:creator>Hasrol Jono, Mohd Nor Hajar</dc:creator>
	<dc:creator>Moketar, Nor Aiza</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning; Big Data; Data Mining</dc:subject>
	<dc:subject xml:lang="en-US">Classification; Data visualization; Naïve Bayes; Twitter sentiment analysis</dc:subject>
	<dc:description xml:lang="en-US">Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universiti Teknologi MARA Cawangan Melaka</dc:contributor>
	<dc:date>2023-12-01</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22647</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i4.pp1793-1802</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 4: December 2023; 1793-1802</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22647/13754</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/22647/3124</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/22647/3125</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>