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Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 15 No. 1 (2022) · pp. 39–46

DOI
10.21609/jiki.v15i1.1044
ISSN
2502-9274, 2088-7051
Published
2022-02-27
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media. One of the social media that is most often used by Indonesian people to express their opinion is Twitter. The method used in this research is Bidirectional LSTM which will be combined with word embedding. In this study, fastText and GloVe were tested as word embedding. We created 8 test scenarios to inspect performance of the word embeddings, using both pre-trained and self-trained word embedding vectors. Dataset gathered from Twitter was prepared as stemmed dataset and unstemmed dataset. The highest accuracy from GloVe scenario group was generated by model which used self-trained GloVe and trained on unstemmed dataset. The accuracy reached 92.5%. On the other hand, the highest accuracy from fastText scenario group generated by model which used self-trained fastText and trained on stemmed dataset. The accuracy reached 92.3%. In other scenarios that used pre-trained embedding vector, the accuracy was quite lower than scenarios that used self-trained embedding vector, because the pre-trained embedding data was trained using the Wikipedia corpus which contains standard and well-structured language while the dataset used in this study came from Twitter which contains non-standard sentences. Even though the dataset was processed using stemming and slang words dictionary, the pre-trained embedding still can not recognize several words from our dataset.

Keywords

Collaboration network of the authors

25 authors · 51 co-authorship links · 3 across different organisations

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

  • Universitas Amikom Yogyakarta
  • Universitas AKPRIND Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ahmad Fikri IskandarUniversitas Amikom Yogyakarta3
Wahyu HidayatUniversitas Amikom Yogyakarta3
Anggit Dwi HartantoUniversitas Amikom Yogyakarta3
Catur Riyono Heri WibowoUniversitas Amikom Yogyakarta1
HanafiAffiliation unknown1
Agung Budi PrasetioUniversitas Amikom Yogyakarta1
Andi SunyotoUniversitas Amikom Yogyakarta1
Mursyid ArdiansyahUniversitas Amikom Yogyakarta1
Suwanto RaharjoUniversitas AKPRIND Indonesia1
Agung Budi PrasetyoUniversitas Amikom Yogyakarta1
Veny Cahya HarditaUniversitas Amikom Yogyakarta1
Emha Taufiq LuthfiUniversitas Amikom Yogyakarta1
Wahyu Krisna HidayatUniversitas Amikom Yogyakarta1
Agung Prasetio BudiUniversitas Amikom Yogyakarta1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Arief SetyantoUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1
Mei KurniawanAffiliation unknown1
M. SuyantoAffiliation unknown1
KusriniAffiliation unknown1
Aiman MukhlishahAffiliation unknown1
Erwin SyahrudinUniversitas Amikom Yogyakarta1

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://jiki.cs.ui.ac.id/index.php/jiki/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">Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings</dc:title>
	<dc:creator>Agustiningsih, Kartikasari Kusuma</dc:creator>
	<dc:creator>Utami, Ema</dc:creator>
	<dc:creator>Alsyaibani, Muhammad Altoumi</dc:creator>
	<dc:subject xml:lang="en-US">Sentiment Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Twitter</dc:subject>
	<dc:subject xml:lang="en-US">Bidirectional LSTM</dc:subject>
	<dc:subject xml:lang="en-US">Word Embedding</dc:subject>
	<dc:subject xml:lang="en-US">fastText</dc:subject>
	<dc:subject xml:lang="en-US">GloVe</dc:subject>
	<dc:description xml:lang="en-US">Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media. One of the social media that is most often used by Indonesian people to express their opinion is Twitter. The method used in this research is Bidirectional LSTM which will be combined with word embedding. In this study, fastText and GloVe were tested as word embedding. We created 8 test scenarios to inspect performance of the word embeddings, using both pre-trained and self-trained word embedding vectors. Dataset gathered from Twitter was prepared as stemmed dataset and unstemmed dataset. The highest accuracy from GloVe scenario group was generated by model which used self-trained GloVe and trained on unstemmed dataset. The accuracy reached 92.5%. On the other hand, the highest accuracy from fastText scenario group generated by model which used self-trained fastText and trained on stemmed dataset. The accuracy reached 92.3%. In other scenarios that used pre-trained embedding vector, the accuracy was quite lower than scenarios that used self-trained embedding vector, because the pre-trained embedding data was trained using the Wikipedia corpus which contains standard and well-structured language while the dataset used in this study came from Twitter which contains non-standard sentences. Even though the dataset was processed using stemming and slang words dictionary, the pre-trained embedding still can not recognize several words from our dataset.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2022-02-27</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://jiki.cs.ui.ac.id/index.php/jiki/article/view/1044</dc:identifier>
	<dc:identifier>10.21609/jiki.v15i1.1044</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 15 No. 1 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 39-46</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v15i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1044/454</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Jurnal Ilmu Komputer dan Informasi</dc:rights>
</oai_dc:dc>