Journal
Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection
Scientific Journal of Informatics
ISSN 2407-7658, 2460-0040
Published 2025-03-24
Publisher Universitas Negeri Semarang
Language eng
Abstract
Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature. Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation. Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106. Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.
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Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en">Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection</dc:title>
<dc:creator xml:lang="en">Ningsih, Maylinna Rahayu </dc:creator>
<dc:creator xml:lang="en">Unjung, Jumanto</dc:creator>
<dc:subject xml:lang="en">Anxiety</dc:subject>
<dc:subject xml:lang="en">H2O Gradient Boosting</dc:subject>
<dc:subject xml:lang="en">Analysis Sentiment</dc:subject>
<dc:subject xml:lang="en">TF-IDF Vectorization</dc:subject>
<dc:description xml:lang="en">Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature.
Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation.
Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106.
Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.</dc:description>
<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
<dc:date>2025-03-24</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">Peer-reviewed Article</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/20582</dc:identifier>
<dc:identifier>10.15294/sji.v12i1.20582</dc:identifier>
<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 4: Special Issue; 1137-1144</dc:source>
<dc:source>2407-7658</dc:source>
<dc:source>2460-0040</dc:source>
<dc:source>10.15294/sji.v11i4</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/20582/3318</dc:relation>
<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>
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3 citations
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Cite this article
APA 7
Ningsih, M. R., & Unjung, J. (2025). Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection. Scientific Journal of Informatics, 11(4), 1137–1144. https://doi.org/10.15294/sji.v12i1.20582
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IEEE
M. R. Ningsih and J. Unjung, “Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection,” Scientific Journal of Informatics, vol. 11, no. 4, pp. 1137–1144, 2025, doi: 10.15294/sji.v12i1.20582.
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