Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction

Abstract

Early detection of suicidal ideation in depressed individuals can allow for adequate medical attention and support, which in many cases is life-saving. Recent NLP research focuses on classifying, from a given piece of text, if an individual is suicidal or clinically healthy. However, there have been no major attempts to differentiate between depression and suicidal ideation, which is an important clinical challenge. Due to the scarce availability of EHR data, suicide notes, or other similar verified sources, web query data has emerged as a promising alternative. Online sources, such as Reddit, allow for anonymity that prompts honest disclosure of symptoms, making it a plausible source even in a clinical setting. However, these online datasets also result in lower performance, which can be attributed to the inherent noise in web-scraped labels, which necessitates a noiseremoval process. Thus, we propose SDCNL, a suicide versus depression classification method through a deep learning approach. We utilize online content from Reddit to train our algorithm, and to verify and correct noisy labels, we propose a novel unsupervised label correction method which, unlike previous work, does not require prior noise distribution information. Our extensive experimentation with multiple deep word embedding models and classifiers display the strong performance of the method in a new, challenging classification application. We make our code and dataset available at https://github.com/ayaanzhaque/SDCNL

Mental Health Analysis in Social Media Posts: A Survey

“…Textual Feature Extraction The traditional methods of converting text in vectors (TFEx) is performed with conventional approach of TFIDF vectorizer, Count vectorizer, and Hashing vectorizer [42]. For dimensionality reduction, the selective features are processed further by using PCA, NMF and other filter based linear feature selection algorithms.…”

“…Feature Embedding With advancements in the word to vector conversion using neural network approach, the word-2vec [98], the GloVe [10,11], and the Fasttext are encode the text. To handle the longer text like phrase, sentence or paragraph, the researchers use BERT [99], Sentence-BERT [100], and Google Universal Sentence Encoder (GUSE) [101] for feature vector representation [42].The use of embedding over dense layers, BERT, GUSE, and GRU [11, [114] 2022 ✓ ✓ ✓ Depression Garg et al [114] 2022 ✓ ✓ Depression Yang et al [85] 2022 ✓ ✓ Depression Fig. 6 Feature vector representation for mental health analysis in social media posts vectors.…”

“…However, Ref. [42] suggests the problem of noisy labels and address it by introducing a new dataset on depression versus suicide. In extension to this, a data augmentation approach resolve the problem of limited data availability for mental health analysis [138].…”

“…Noisy Labels: We found that the potential of some labels of data is found to be corrupted in the past which are mentioned as the noisy labels. To solve this problem, SDCNL model introduced a unique feature of label correction methodology to classifying posts as suicide versus depression [42]. 2.…”