Original scientific paper
https://doi.org/10.1080/00051144.2023.2296793
An evolutionary approach for depression detection from Twitter big data using a novel deep learning model with attention based feature learning mechanism
K Prabhakar
; Dept. of CSE, School of Engineering and Technology, CMR University, Bangalore, India
*
V Kavitha
; Department of Computer Science and Engineering, University College of Engineering Kancheepuram, Kancheepuram, India
* Corresponding author.
Abstract
One of the main factors causing suicide is depression. However, many cases of depression go
undiagnosed because they are not correctly diagnosed. An increasing number of people with
mental illnesses express their emotions online using tools like social media (SM) and specialized
websites. Recently, efforts have been made to use Machine Learning (ML) and deep learning (DL)
models to predict depression from SM platforms. However, it is problematic that most ML algorithms now provide no explanation. As a result, this study proposes a novel Deep Learning (DL)
model called residual network 50, which includes optimal long short-term memory (RNT-OLSTM)
for Depression Detection (DD) on Twitter data. In addition, to address the issue of data imbalance
in the Twitter data, a cluster-based oversampling approach is used, which considerably reduces
the possibility of bias towards the dominant class (non-depressed).. Finally, the embedding layers
are inputted to RNT-OLSTM for DD, in which the hyperparameters of the network are tuned using
the Sine Chaotic map and constriction factor-based Coyote Optimization Algorithm (SCCOA) to
minimize the prediction loss. The out-comes prove that the proposed system performs better
than the existing schemes for the DD of imbalanced Twitter data with higher detection rates.
Keywords
Depression detection; Twitter; social media; big data; deep learning; machine learning; data balancing; and word embedding
Hrčak ID:
323036
URI
Publication date:
10.1.2024.
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