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Erschienen in:
Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Analysis of Social Media Posts for Early Detection of Mental Health Conditions

verfasst von : Antoine Briand, Hayda Almeida, Marie-Jean Meurs

Erschienen in: Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

This paper presents a multipronged approach to predict early risk of mental health issues from user-generated content in social media. Supervised learning and information retrieval methods are used to estimate the risk of depression for a user given the content of its posts in reddit. The approach presented here was evaluated on the CLEF eRisk 2017 pilot task. We describe the details of five systems submitted to the task, and compare their performance. The comparisons show that combining information retrieval and machine learning methods gives the best results.

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Vorheriges Kapitel Reranking Candidate Lists for Improved Lexical Induction
Nächstes Kapitel Motor Bearing Fault Diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal
Fußnoten
7
Obtained from a conceptual feature map in SenticNet [5].
 
8
Obtained from Wikipedia pages for “depression”, “psychoactive drugs”, and “list of antidepressants”.
 
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Metadaten
Titel
Analysis of Social Media Posts for Early Detection of Mental Health Conditions
verfasst von
Antoine Briand
Hayda Almeida
Marie-Jean Meurs
Copyright-Jahr
2018
Buch
Advances in Artificial Intelligence

Print ISBN: 978-3-319-89655-7

Electronic ISBN: 978-3-319-89656-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-89656-4_11