Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Kishaloy Halder, Lahari Poddar, Min-Yen Kan


Abstract
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.
Anthology ID:
W17-5217
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
WASSA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
127–135
Language:
URL:
https://www.aclweb.org/anthology/W17-5217
DOI:
10.18653/v1/W17-5217
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5217.pdf