ConvSent at CLPsych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on Reddit

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

Abstract

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.
在Reddit上使用后级情感特征进行自杀风险预测
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。
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