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All Journal JINAV: Journal of Information and Visualization
Andre Agasi Simanungkalit
Informatics Study Program, Informatics Faculty, Telkom University Bandung, Jawa Barat, 40257, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics

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Depression Detection on Twitter Social Media Platform using Bidirectional Long-Short Term Memory Andre Agasi Simanungkalit; Warih Maharani; Prati Hutari Gani
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1503

Abstract

Depression is one of the mental disorders that are often experienced by a person in daily life. Social media platforms is a new thing as an alternative to tell stories and express current feelings by people today. Twitter is one of the social media that is often used to express feelings and opinions through tweets posts, including tweets that contain hate speech which indirectly shows symptoms of depressive disorder through statements uploaded. It also requires modeling that can recognize users with the potential to experience depression so that they can get initial treatment. This can be implemented using the BiLSTM (Bidirectional Long Short-Term Memory) method and the Word2Vec feature. It can be concluded that the dimensional size of the large feature word2vec, LSTM, and Conv1d layers influenced the model in detecting depression which can be seen in the testing accuracy and F-1 score according to the split data used.
Co-Authors Prati Hutari Gani Warih Maharani