MGL-CNN: A Hierarchical Posts Representations Model for Identifying Depressed Individuals in Online Forums

Abstract

More users suffering from depression turn to online forums to express their problems and seek help. In such forums, there is often a large volume of posts with sensitive content, indicating that the user has a risk of suicide and self-harm. Early detection of depression using appropriate deep learning models and social media data can prevent potential self-harm. However, existing depression detection models are not powerful enough to capture critical sentiment information from the large volume of posts published by each user, which makes the performance of these models not satisfying. To address this problem, we propose a hierarchical posts representations model named Multi-Gated LeakyReLU CNN (MGL-CNN) for identifying depressed individuals in online forums. The model consists of two parts: the first one is a post-level operation, which is used to learn the representation of each post of the user, and the second one is a user-level operation, which is used to obtain the overall representation of the user's emotional state. Besides, we propose another depression detection model by changing the number of gated units in the MGL-CNN, which is named Single-Gated LeakyReLU CNN (SGL-CNN). We show how to use our models to identify depressed users through a lot of posted content. Experimental results showed that our models performed better than the previous stateof-the-art models on the Reddit Self-reported Depression Diagnosis dataset, and also performed well on the Early Detection of Depression dataset.

Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

“…With the rapid development of social media, the public can share their emotion, opinion, medical experience, and professional knowledge on public health issues such as infectious disease prevention [1,2], drug safety supervision [3,4], health promotion [5][6][7], and vaccination [8][9][10][11].…”

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

“…A bag-of-words [4,11,19], text representation that depicts the presence of words within a document. It involves two things: (1) Known words vocabulary.…”

“…Feature extraction technique Manoj Sethi [2], Yuwen Lyu [12], Nafiz Al Asad [13], Subhan Tariq [18], Kashif Ayyab [43], Hay Mar Su Aung [74], Alex M. G. Almeida [77], Govin Gaikwad [79], Tianyi Wang [83], Masum Billah [94], Rinki Chatterjee [95] Term Frequency Inverse Document Frequency (TF-IDF) Ganzalo A. Ruz [4], Li Chen Cheng [11], Guozheng Rao [19], Rıza Velioglu [97], Md. Mokhlesur Rahmana [100] Bag of Words (BOW) Ganzalo A. Ruz [4], Li Chen Cheng [11], Md.…”

“…CNN: Convolutional neural network [14,15,19,31,45] is the deep learning method that have become using in various dee learning or image processing tasks and is attracting interest across a variety of areas and it consists of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. Backpropagation algorithm helps in automatically designed and adaptively learn spatial hierarchies of features.…”

DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media

“…Use of a deep neural network (DNN) such as convolutional neural networks (CNNs) and long-short short-term memory (LSTMs) Hochreiter and Schmidhuber [18] have made notable progress in detecting mental illness on social media [7,35,41,48]. Deep learning has obtained impressive results in natural language processing (NLP) tasks such as text classification and sentiment analysis.…”