Classification of Depression Expressions on Twitter Using Ensemble Learning with Word2Vec

  • Muhammad Reza Adi Nugraha School of Computing, Telkom University, Bandung
  • Yuliant Sibaroni School of Computing, Telkom University, Bandung
https://doi.org/10.25139/inform.v9i1.7559
Abstract views: 383 , PDF downloads: 287
Keywords: Depression Expression, Twitter Datasets, Ensemble Learning, Word2vec

Abstract

One of the mental health disorders experienced by people is depression. Depression is a mental disorder characterized by feelings of sadness, loss of interest or pleasure in daily activities, and decreased cognitive function that can affect social life, work, and general physical health. Early detection is needed to prevent the occurrence of bad risks. One of the early detections can be done through social media. This is because social media is one of the tools used to channel expression. This research uses data taken from Twitter social media to create a machine learning model. Before model building, data pre-processing will be carried out using Word2Vec to convert text into a continuous vector representation. The algorithm used is ensemble learning by combining five algorithms: Logistic Regression, Decision Tree, K-Nearest Neighbor, Artificial Neural Network, and Support Vector Machine. The results show that different Word2Vec architectures can give the model another performance. Ensemble Learning can improve the performance of using a single model. The best results were obtained by using a data ratio of 90:10 using the Skip-gram architecture to get an accuracy value and f1-score of 94%.

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Published
2024-01-28
How to Cite
Adi Nugraha, M. R., & Sibaroni, Y. (2024). Classification of Depression Expressions on Twitter Using Ensemble Learning with Word2Vec. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 9(1), 67-74. https://doi.org/10.25139/inform.v9i1.7559
Issue
Vol. 9 No. 1 (2024)
Section
Articles

Copyright (c) 2024 Muhammad Reza Adi Nugraha, Yuliant Sibaroni

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