Citation

Tadesse MM, Lin H, Xu B, Yang L. Algorithms 2020; 13(1).

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/a13010007

PMID

unavailable

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. © 2019 by the authors.

Language: en

Keywords

Risk assessment; Suicide ideation; Learning systems; Social networking (online); Classification results; Classification (of information); Social media; Machine learning; Deep learning; Long short-term memory; Network architecture; Automatic recognition; Classification approach; Combined neural networks; Early suicide detection; Embeddings; Learning architectures; Linguistic metadata; Reddit social media; Text processing; Word embedding