Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
复制标题
心理健康论坛中情绪状态的时间进展建模：循环神经网络方法
DOI：
10.18653/v1/w17-5217
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Min
中科院分区：
文献类型：
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作者：
Kishaloy Halder;Lahari Poddar;Min
患者求助于在线健康社区，不仅是为了获得有关具体情况的信息，也是为了获得情感支持。以前的研究表明，情绪状态的发展可以通过个人帖子的语言模式来研究。我们分析了来自HealthBoards.com的心理健康部分的真实世界数据集。从他们帖子中的词汇用法估计，我们发现患者之间的情绪进步差异很大。我们研究了从病人过去的帖子中预测她未来的情绪状态的问题，并提出了一个基于递归神经网络(RNN)的体系结构来解决这个问题。我们发现，考虑到她的历史帖子和参与特征，未来的情绪状态可以得到合理的预测。我们的评估结果证明了我们所提出的体系结构的有效性，其性能优于最先进的方法，平均绝对误差降低超过0.13。
Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual’s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient’s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.