在Reddit上使用后级情感特征进行自杀风险预测

Kristen Allen, Shrey Bagroy, Alexander L Davis, T. Krishnamurti
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引用次数: 10

摘要

本研究旨在从公共文本中推断心理健康状况,以便早期发现自杀风险。它通过预测Reddit子论坛r/SuicideWatch上用户的自杀风险,为2019年CLPsych研讨会的共享任务A做出贡献。我们使用卷积神经网络来整合Reddit帖子级别的LIWC信息,包括讨论的主题、第一人称焦点、情感体验、语法选择和主题风格。在将用户分为四个风险类别时,我们的最佳系统在保留测试集上的宏观平均F1分数为0.50。这项工作展示了语言调查和单词计数词典的预测能力,结合卷积网络和对每个帖子和用户的整体考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit
This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.
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