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

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

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

Abstrak

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%.

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  • Universiti Teknologi MARA
  • Indonesia University of Education
  • State University of Jakarta
  • Yogyakarta State University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Raseeda HamzahUniversiti Teknologi MARA5
Nurqueen Sayang Dinnie WirakarnainUniversiti Teknologi MARA3
Zainab OthmanUniversiti Teknologi MARA3
MunirAfiliasi tidak diketahui2
Anis Amilah ShariUniversiti Teknologi MARA2
Nurul Athirah AhmadUniversiti Teknologi MARA2
Hana Fakhira AlmarzukiUniversiti Teknologi MARA2
Zuhri ArafahUniversiti Teknologi MARA2
Amir Haikal Abdul HalimUniversiti Teknologi MARA2
Herbert SiregarIndonesia University of Education1
Ade SobandiIndonesia University of Education1
Samialloi NusratulloAfiliasi tidak diketahui1
Dwi Novia Al HusaeniAfiliasi tidak diketahui1
Ghina Firdha NabilaAfiliasi tidak diketahui1
Asep WahyudinAfiliasi tidak diketahui1
Yudi WibisonoAfiliasi tidak diketahui1
RasimAfiliasi tidak diketahui1
Nurul Hidayah Mat ZainUniversiti Teknologi MARA1
Siti Rahayu Abdul AzizUniversiti Teknologi MARA1
Norshahidatul Hasana IshakUniversiti Teknologi MARA1
Heny HendrayatiIndonesia University of Education1
Rosniza RoslanUniversiti Teknologi MARA1
Amni Munira KhidirUniversiti Teknologi MARA1
Shafaf IbrahimUniversiti Teknologi MARA1
Raihah AminuddinUniversiti Teknologi MARA1
Nurul Amira Mohd AliUniversiti Teknologi MARA1
Heni MulyaniIndonesia University of Education1
Chew Chiou ShengUniversiti Teknologi MARA1
Nugraha NugrahaIndonesia University of Education1
Fadilah Ezlina ShahbudinUniversiti Teknologi MARA1
David Loh Er FuUniversiti Teknologi MARA1
Masnur AliState University of Jakarta1
Eka Fitri Novita SariState University of Jakarta1
Nofi Marlina SiregarState University of Jakarta1
Sigit Wahyu NugrohoYogyakarta State University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<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>
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