Research on the detection model of mental illness of online forum users based on convolutional network

Bibliographic Data

ID15698386
AuthorsYuliang Guo (0000-0002-8929-0416, Las Positas College, corresponding author), Zheng Zhang (0000-0002-0389-1844, Nanyang Institute of Technology), Xuejun Xu (0009-0008-2640-7331, Hubei University)
Year2023
Volume11
Issue1
Pages424-424
Publication date2023-12-04
Open AccessYes
TypeARTICLE
VenueBMC Psychology (JOURNAL)
Journal identifiersISSN: 2050-7283 • E-ISSN: 2050-7283
PublisherBioMed Central (PUBLISHER • GB)
DOI10.1186/s40359-023-01460-4
PMID38049891
OpenAlexW4389303802
LanguageEN
Citations received1
References cited10

Recently, there will be more than 4.62 billion social media users worldwide. A large number of users tend to publish personal emotional dynamics or express opinions on social media. These massive user data provide data support for the development of mental illness detection research and have achieved good results. However, it is difficult for current mental illness detection models to accurately identify key emotional features from a large number of posts issued by users to detect problem users. In view of the fact that the existing models cannot more accurately extract the words with high emotional contribution in the content of user posts, this paper proposes two hierarchical user post feature representation models, named Single-Gated LeakReLU-CNN (SGL-CNN) and Multi-Gated LeakyReLU-CNN (MGL-CNN). We leverage these 2 models to identify users with mental illness in online forums. For all posts published by each user within a certain time span, the model proposed in this paper can identify key emotional features in them and filter out other unimportant information as much as possible. In addition, the addition of gating units in this paper can significantly improve the performance of emotion detection tasks. The experimental results based on the task of RSDD dataset prove that the performance of the model proposed in this paper is superior to that of the existing methods

Channel (broadcasting · Computer security · Convolutional neural network · Data science · Feature (linguistics · Information retrieval · Key (lock · Leverage (statistics · Machine learning · Mental health · Mental illness · Publication · Social media · Task (project management · World Wide Web · Computer Science · Mental Health Research Topics · Mental Health via Writing · Psychology · Sentiment Analysis and Opinion Mining · Artificial Intelligence

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