Detection of Suicide Ideation in Social Media Forums Using Deep Learning
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TLDR
This study addresses the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media by employing an LSTM-CNN combined model to evaluate and compare to other classification models.Abstract:
Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.read more
Citations
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Journal ArticleDOI
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
TL;DR: This paper is the first survey that comprehensively introduces and discusses the methods from these categories of suicidal ideation detection, and summarizes the limitations of current work and provides an outlook of further research directions.
Journal ArticleDOI
Natural language processing applied to mental illness detection: a narrative review
TL;DR: In this article , the authors provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions, and also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.
Journal ArticleDOI
Using Social Media for Mental Health Surveillance: A Review
Ruba Skaik,Diana Inkpen +1 more
TL;DR: Big data research of social media data may also support standard surveillance approaches and provide decision-makers with usable information about users' habits and activities.
Proceedings ArticleDOI
A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media.
TL;DR: This work proposes STATENet, a time-aware transformer based model for preliminary screening of suicidal risk on social media, which outperforms competitive methods, demonstrating the utility of emotional and temporal contextual cues for suicide risk assessment.
Journal ArticleDOI
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
TL;DR: Suicide is a critical issue in modern society as discussed by the authors, and early detection and prevention of suicide attempts should be addressed to save people's life by early detection of suicidal ideation.
References
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.