Depression Detection on Twitter Using Bidirectional Long Short Term Memory
-
Putri Ester Sumolang
*
Telkom University, Bandung,
Indonesia
- Warih Maharani Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
The usage of social media as a platform for individual expression is one example of how the advancement of technology has an impact on society. Therefore, many Twitter users show symptoms of depressive disorders through their tweets. It is crucial to be aware of the need to consult a doctor or other specialists to prevent suicides. However, leveraging Twitter user tweet data to detect depression early on can be avoided by using the Bidirectional Long Short Term Memory (BILSTM) approach and the word2vec feature extraction method. The dataset utilized in this study was obtained from respondents who agreed to have their data used in research after completing a questionnaire based on the Depression Anxiety and Stress Scales - 42 (DASS-42). The whole data from 159 users of Twitter who have been classified as depressed or normal based on the results of the DASS-42 labeling are then preprocessed so that the data can be entered into the word2vec feature extraction and modeled by BiLSTM as a classification. The evaluation revealed an accuracy of 83.46 % and an f1-score of 87.11 %. By increasing the number of neurons, accuracy increased by 2.36 %, and f1-score climbed by 1.64 %.
Downloads
References
S. Mutmainah, “KEMUNGKINAN DEPRESI DARI POSTINGAN PADA SOSIAL MEDIA,” J. Sains, Nalar, dan Apl. Teknol. Inf., vol. 1, no. 2, 2022.
WHO, “Mental health,” WHO, 2021. https://www.who.int/health-topics/mental-health (accessed Oct. 16, 2021).
A. A. Rachmawati, “Darurat Kesehatan Mental bagi Remaja - Environmental Geography Student Association,” egsaugm, 2020. https://egsa.geo.ugm.ac.id/2020/11/27/darurat-kesehatan-mental-bagi-remaja/ (accessed Oct. 16, 2021).
D. S. Oktarini, “Curhat di Twitter Saat Depresi, Pria Ini Ditemukan Bunuh Diri,” suara.com, 2019. https://www.suara.com/tekno/2019/01/28/103735/curhat-di-twitter-saat-depresi-pria-ini-ditemukan-bunuh-diri?page=all (accessed Dec. 07, 2022).
S. R. Kamite and V. B. Kamble, “Detection of Depression in Social Media via Twitter Using Machine learning Approach,” 2020. doi: 10.1109/ICSIDEMPC49020.2020.9299641.
F. M. Shah et al., “Early Depression Detection from Social Network Using Deep Learning Techniques,” 2020. doi: 10.1109/TENSYMP50017.2020.9231008.
A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, 2020, doi: 10.22146/ijccs.51743.
E. Kang, “Long Short-Term Memory (LSTM): Concept,” Medium.com, 2017. https://medium.com/@kangeugine/long-short-termmemory-%0Alstm-concept-cb3283934359 (accessed Oct. 14, 2021).
M. Soni, “Understanding architecture of LSTM cell from scratch with code.,” Medium.com, 2018. https://medium.com/hackernoon/understanding-architecture-of-lstm-cell-fromscratch-%0Awith-code-8da40f0b71f4 (accessed Nov. 18, 2021).
S. Kusumadewi and H. Wahyuningsih, “Model Sistem Pendukung Keputusan Kelompok untuk Penilaian Gangguan Depresii, Kecemasan dan Stress Berdasarkan DASS-42,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 2, 2020, doi: 10.25126/jtiik.2020721052.
K. S. Nugroho, “Dasar Text Preprocessing dengan Python,” Medium.com, 2019. https://ksnugroho.medium.com/dasar-text-preprocessing-dengan-python-a4fa52608ffe (accessed Nov. 27, 2021).
C. Khanna, “Text pre-processing: Stop words removal using different libraries,” Medium.com, 2021. https://towardsdatascience.com/text-pre-processing-stop-wordsremoval-%0Ausing-different-libraries-f20bac19929a (accessed Nov. 28, 2021).
A. Firdaus, “Cara Kerja Word2vec,” Medium.com, 2019. https://medium.com/@afrizalfir/mengenal-word2vec-af4758da6b5d (accessed Nov. 29, 2021).
A. M. Ertugrul and P. Karagoz, “Movie Genre Classification from Plot Summaries Using Bidirectional LSTM,” in Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018, 2018, vol. 2018-January. doi: 10.1109/ICSC.2018.00043.
P. Bahad, P. Saxena, and R. Kamal, “Fake News Detection using Bi-directional LSTM-Recurrent Neural Network,” in Procedia Computer Science, 2019, vol. 165. doi: 10.1016/j.procs.2020.01.072.
S. Narkhede, “Understanding Confusion Matrix,” Medium.com, 2018. https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 (accessed Nov. 27, 2021).
H. N B, “Confusion Matrix, Accuracy, Precision, Recall, F1 Score,” Medium.com, 2019. https://medium.com/analytics-vidhya/confusion-matrix-accuracyprecision-%0Arecall-f1-score-ade299cf63cd (accessed Nov. 27, 2021).
N. L. P. P. Waisnawa, D. Nurjanah, and H. Nurrahmi, “Cyberbullying Detection on Twitter using Support Vector Machine Classification Method,” Build. Informatics, Technol. Sci., vol. 3, no. 4 SE-Articles, Mar. 2022, doi: 10.47065/bits.v3i4.1435.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Depression Detection on Twitter Using Bidirectional Long Short Term Memory
Pages: 369-376
Copyright (c) 2022 Putri Ester Sumolang, Warih Maharani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
