Sentiment Analysis of YouTube Comments on Indonesian 2024 Presidential Candidate Talk Show Using 1D-CNN, LSTM, and BiLSTM Models.
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| Title: | Sentiment Analysis of YouTube Comments on Indonesian 2024 Presidential Candidate Talk Show Using 1D-CNN, LSTM, and BiLSTM Models. |
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| Authors: | Amaliaputri, Hasna Azzahra1 hasnazhrr@gmail.com, Wijaya, Andreas Parama2 a.p.wijaya@unpar.ac.id, Salim, Daniel2 daniel.salim@unpar.ac.id |
| Source: | IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2697-2703. 7p. |
| Subjects: | Sentiment analysis, Deep learning, YouTube (Web resource), Elections, Political communication, Long short-term memory |
| Abstract: | The rapid growth of social media has significantly transformed political communication, with YouTube emerging as a key platform for public engagement. User-generated comments, in particular, offer valuable insights into public sentiment toward political candidates. This study analyzes sentiment in YouTube comments related to the 2024 Indonesian presidential election, with a focus on videos featuring Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo. Two word embedding methods -- Keras Embedding and FastText -- are evaluated in combination with three deep learning architectures: one-dimensional convolutional neural networks (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). Model performance is assessed using accuracy and F1-score. The experimental results demonstrate that the BiLSTM model, combined with Keras Embedding, achieves the most consistent and competitive performance across all datasets. This model is subsequently applied to a dataset of comments collected from videos published in early 2024. The findings indicate that videos featuring Prabowo Subianto receive the highest proportion of positive sentiment, followed by those featuring Anies Baswedan, while videos featuring Ganjar Pranowo receive the lowest positive sentiment. These results highlight the effectiveness of deep learning-based sentiment analysis in gauging public opinion in an electoral context. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195026902 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sentiment Analysis of YouTube Comments on Indonesian 2024 Presidential Candidate Talk Show Using 1D-CNN, LSTM, and BiLSTM Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Amaliaputri%2C+Hasna+Azzahra%22">Amaliaputri, Hasna Azzahra</searchLink><relatesTo>1</relatesTo><i> hasnazhrr@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Wijaya%2C+Andreas+Parama%22">Wijaya, Andreas Parama</searchLink><relatesTo>2</relatesTo><i> a.p.wijaya@unpar.ac.id</i><br /><searchLink fieldCode="AR" term="%22Salim%2C+Daniel%22">Salim, Daniel</searchLink><relatesTo>2</relatesTo><i> daniel.salim@unpar.ac.id</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Jul2026, Vol. 56 Issue 7, p2697-2703. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22YouTube+%28Web+resource%29%22">YouTube (Web resource)</searchLink><br /><searchLink fieldCode="DE" term="%22Elections%22">Elections</searchLink><br /><searchLink fieldCode="DE" term="%22Political+communication%22">Political communication</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rapid growth of social media has significantly transformed political communication, with YouTube emerging as a key platform for public engagement. User-generated comments, in particular, offer valuable insights into public sentiment toward political candidates. This study analyzes sentiment in YouTube comments related to the 2024 Indonesian presidential election, with a focus on videos featuring Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo. Two word embedding methods -- Keras Embedding and FastText -- are evaluated in combination with three deep learning architectures: one-dimensional convolutional neural networks (1D-CNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). Model performance is assessed using accuracy and F1-score. The experimental results demonstrate that the BiLSTM model, combined with Keras Embedding, achieves the most consistent and competitive performance across all datasets. This model is subsequently applied to a dataset of comments collected from videos published in early 2024. The findings indicate that videos featuring Prabowo Subianto receive the highest proportion of positive sentiment, followed by those featuring Anies Baswedan, while videos featuring Ganjar Pranowo receive the lowest positive sentiment. These results highlight the effectiveness of deep learning-based sentiment analysis in gauging public opinion in an electoral context. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 2697 Subjects: – SubjectFull: Sentiment analysis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: YouTube (Web resource) Type: general – SubjectFull: Elections Type: general – SubjectFull: Political communication Type: general – SubjectFull: Long short-term memory Type: general Titles: – TitleFull: Sentiment Analysis of YouTube Comments on Indonesian 2024 Presidential Candidate Talk Show Using 1D-CNN, LSTM, and BiLSTM Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Amaliaputri, Hasna Azzahra – PersonEntity: Name: NameFull: Wijaya, Andreas Parama – PersonEntity: Name: NameFull: Salim, Daniel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19929978 Numbering: – Type: volume Value: 56 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Applied Mathematics Type: main |
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