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A PRELIMINARY STUDY OF SENTIMENT ANALYSIS ON COVID-19 NEWS: LESSON LEARNED FROM DATA ACQUISITION, PRE-PROCESSING, AND DESCRIPTIVE ANALYTICS

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 4 (2023) · pp. 1901–1914

DOI
10.30598/barekengvol17iss4pp1901-1914
ISSN
2615-3017, 1978-7227
Published
2023-12-18
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Sentiment analysis is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Web scraping was employed to obtain the news article about Covid-19 in Indonesia. In order to select the representative news, two-step sampling was employed by using stratified and systematic random sampling. According to the topic modelling results using lambda 0.6, news articles are grouped into three topics: updating Covid-19 cases, vaccination, and government policy. In addition, based on the number of positive and negative words, news articles are grouped into news dominated by positive words, news dominated by negative words, and news with the same number of positive and negative words. Methods for representing text in numerical form have been developed. Some of them use tf-idf weighting and word embedding. It does not pay attention to word order or meaning, only based on the frequency of words both locally and globally. Furthermore, this method will form a vector size as large as the number of unique words in the document, so it is less effective when many documents are used. Meanwhile, the vector size generated from the word2vec method is not as much as the number of unique words in the corpus. In addition, word2vec considers the context of the words in the corpus.

Collaboration network of the authors

40 authors · 124 co-authorship links · 8 across different organisations · showing the 40 most connected

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

  • IPB University
  • Bandung Islamic University
  • Binus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bagus SartonoIPB University11
Yenni AngrainiIPB University9
Laily Nissa Atul MualifahAffiliation unknown7
Anang KurniaAffiliation unknown6
Indahwati IndahwatiAffiliation unknown3
Aji Hamim WigenaAffiliation unknown2
Asep SaefuddinIPB University2
Erfiani ErfianiAffiliation unknown2
Dwi Agustin Nuriani SirodjAffiliation unknown2
Budi SusetyoAffiliation unknown2
I Made SumertajayaAffiliation unknown2
Agus Mohamad SolehIPB University2
Anwar FitriantoAffiliation unknown2
Asysta Amalia PasaribuBinus University2
Teguh PrasetyoIPB University1
Rizki Alifah PutriIPB University1
Dini RamadhaniIPB University1
Wa Ode Rona FreyaAffiliation unknown1
Vera Maya SantiAffiliation unknown1
Embay RohaetiAffiliation unknown1
Sitti NurhalizaAffiliation unknown1
Hafizah IlmaAffiliation unknown1
Andika Putri RatnasariAffiliation unknown1
Khusnia Nurul KhikmahAffiliation unknown1
Asrirawan AsrirawanAffiliation unknown1
Leli LatifahAffiliation unknown1
Vivin Nur AzizaAffiliation unknown1
Fatma Hilali Moh'dAffiliation unknown1
Firda Aulia MaghfirohAffiliation unknown1
Muhammad Azis SuprayogiAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Arie PurwantoAffiliation unknown1
Indra Rivaldi SiregarAffiliation unknown1
Adhiyatma NugrahaAffiliation unknown1
Riska YuliantiAffiliation unknown1
Nabila Tri AmandaAffiliation 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) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A PRELIMINARY STUDY OF SENTIMENT ANALYSIS ON COVID-19 NEWS: LESSON LEARNED FROM DATA ACQUISITION, PRE-PROCESSING, AND DESCRIPTIVE ANALYTICS</dc:title>
	<dc:creator>Amalia, Rahmatin Nur</dc:creator>
	<dc:creator>Sadik, Kusman</dc:creator>
	<dc:creator>Notodiputro, Khairil Anwar</dc:creator>
	<dc:description xml:lang="en-US">Sentiment analysis is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Web scraping was employed to obtain the news article about Covid-19 in Indonesia. In order to select the representative news, two-step sampling was employed by using stratified and systematic random sampling. According to the topic modelling results using lambda 0.6, news articles are grouped into three topics: updating Covid-19 cases, vaccination, and government policy. In addition, based on the number of positive and negative words, news articles are grouped into news dominated by positive words, news dominated by negative words, and news with the same number of positive and negative words. Methods for representing text in numerical form have been developed. Some of them use tf-idf weighting and word embedding. It does not pay attention to word order or meaning, only based on the frequency of words both locally and globally. Furthermore, this method will form a vector size as large as the number of unique words in the document, so it is less effective when many documents are used. Meanwhile, the vector size generated from the word2vec method is not as much as the number of unique words in the corpus. In addition, word2vec considers the context of the words in the corpus.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-12-18</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8951</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss4pp1901-1914</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1901-1914</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss4year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8951/7166</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Rahmatin Nur Amalia, Kusman Sadik, Khairil Anwar Notodiputro</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>