During the week 7 and week 8, I focused on Assessment 3 Annotated Bibliography and Reflection by doing further investigation on research papers to address research questions.

I found interesting research articles related to my topic. I searched articles related to Mental Health and Machine learning. I used “Mental Health”, “Mental disorder identification”, “Machine Learning”, “text analysis”, “Natural Language Processing” key words to find research papers in Google scholar, Springer, Wiley, and IEEE Xplore. By reading abstract, it was easy to find the suitable articles for my research.

Here are the methods used to train the model in related articles as below.

Article	Methods
Adrian, A. M., & Sanger, J. B. (2024). Depression Detection Using Machine Learning	Random Forest, Support Vector Machine, Decision Trees
Ananthakrishnan, G. et al. (2022). Suicidal intention detection in tweets using BERT-based transformers	BERT (Bidirectional Encoder Representations from Transformers)
Chadha, A. et al. (2022). Suicidal ideation detection on social media: a machine learning approach	Support Vector Machine, Logistic Regression, and AdaBoost
Chadha, A., & Kaushik, B. (2022). A hybrid deep learning model using grid search and cross-validation for suicidal ideation	Hybrid model (CNN-LSTM) with Grid Search and Cross-Validation
Chatterjee, M. et al. (2022). Suicide ideation detection using multiple feature analysis from Twitter data	Logistic Regression, SVM, Random Forest, XGBoot
Haque, R. et al. (2022). Comparative analysis on suicidal ideation detection using NLP and deep learning	CNN (Convolutional Neural Network), LSTM (Long-Short Term Memory), Bi-directional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Bi-directional GRU (BiGRU), and combined model of CNN and LSTM (C-LSTM),Logistic Regression, Support Vector Classifier, Random Forest, Multinomial Naive Bayes
Li, Z. et al. (2023). Suicide tendency prediction from psychiatric notes using transformer models	Bio-ClinicalBERT, Logistic Regression
Santos, W. et al. (2023). Mental health prediction from social media text using mixture of experts	Mixture of Experts (MoE)
Santos, W. R. D. et al. (2024). SetembroBR: Depression and anxiety disorder prediction	BERT (Bidirectional Encoder Representations from Transformers)

I created annotated bibliography by focusing description of article, methodology, findings , critical evaluation and value to my project for each articles. by reading abstract, conclusion and introduction, I was able to identify the description of article, methodology and findings. I had to read the article deeper to do the critical evaluation by finding the limitations.

Next, I plan to write reflection, review the assessment 3 again and submit it. After that, I will define dashboard requirements and design dashboard layout.