General Depression Detection Analysis Using IndoBERT Method
Many of the tweets we discover on Twitter are concerning feelings of depression which will be caused by varied things. The amount of tweets additionally continues to increase. To be able to decide however depressed a user is, analysing tweets from users can facilitate with that. The method of analysing the detection of depression can help to supply applicable treatment for users who are detected to own depression. During this paper, the users to be analysed are users who have more than 1000 tweets and are Indonesian tweets. Then, crawling / retrieval of user tweet data is carried out. After that, data pre-processing is done. Once that done, using the IndoBERT method to classify the data obtained. In the end, this paper provides the accuracy value of this detection analysis using the IndoBERT method with an accuracy value of 51% and F1-Score of 31%.
 The Health and Safety Executive. "Work-related stress, anxiety or depression," 2020.
 Mutia, Annisa. "Survei: 68% Orang Depresi Akibat Covid-19," Databoks â€“ Katadata. 17 June 2021. [Online]. Available: https://databoks.katadata.co.id/datapublish/2021/06/17/survei-covid-19-menggangu-kesehatan-jiwa-68-orang-depresi.
 Healthfocus Clinical Psychology Services. "Depression Anxiety and Stress Scale DASS (-42) â€“ Healthfocus Clinical Psychology Services," Healthfocus Clinical Psychology Services. [Online]. Available: https://www.healthfocuspsychology.com.au/tools/dass-42/.
 Islam, Md. R., Kabir, M. A., Ahmed, A., Kamal, A.R. M., Wang, H. and Ulhaq, A. "Depression detection from social network data using machine learning techniques," Health Information Science and Systems/ no. 6. 2018.
 Devlin, J., Chang, M. -W., Lee, K. and Toutanova, K. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota. Association for Computational Linguistics. 2019. pp. 4171-4186.
 Koto, F., Rahimi, A., Lau, J. H. and Baldwin, T. "IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained," in Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain. International Committee on Computational Linguistics. 2020. pp. 757-770.
 Koto, F., Lau, J. H. and Baldwin, T. "Liputan6: A Large-scale Indonesian Dataset for Text Summarization," in Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. Suzhou, China. Association for Computational Linguistics. 2020. pp. 598-608.
 Burdisso, S. G., Errecalde, M. and Montes-y-GÃ³mez, M. "A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams," Expert Systems with Applications. vol. 133. pp. 182-197. 2019.
 Leonard, L. C.. "Web-Based Behavioral Modeling for Continuous User Authentication (CUA)," Advances in Computer. 2017.
 B, H. N.. "Confusion Matrix, Accuracy, Precision, Recall, F1 Score," Analytics Vidhya | Medium, 11 December 2019. [Online]. Available: https://medium.com/analytics-vidhya/confusion-matrix-accuracy-precision-recall-f1-score-ade299cf63cd.
 Lovibond, P. F., Lovibond, S. H., â€œThe structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories.â€ in Behaviour Research and Therapy. vol. 33. pp 335-343. 1995.
 Zhang, L., Wang, S., and Liu, B. â€œDeep learning for sentiment analysis: A survey,â€ in WIREs Data Mining and Knowledge Discovery, vol. 8, no. 4, 2018.
 Siburian, V. W., Mulayana, I. E., â€œPrediksi Harga Ponsem Menggunakan Metode Random Forest,â€ in Prosiding Annual Research Seminar 2018, vol. 4, no. 1, 2018.
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