Hong, S, Cohn, A orcid.org/0000-0002-7652-8907 and Hogg, D (2022) Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms. In: The Tenth International Conference on Learning Representations. The Tenth International Conference on Learning Representations, 25-29 Apr 2022, Online. OpenReview .
Abstract
Graph neural networks (GNNs) are widely used in regression and classification problems applied to text, in areas such as sentiment analysis and medical decision-making processes. We propose a novel form for node attributes within a GNN based model that captures node-specific embeddings for every word in the vocabulary. This provides a global representation at each node, coupled with node-level updates according to associations among words in a transcript. We demonstrate the efficacy of the approach by augmenting the accuracy of measuring major depressive disorder (MDD). Prior research has sought to make a diagnostic prediction of depression levels from patient data using several modalities, including audio, video, and text. On the DAIC-WOZ benchmark, our method outperforms state-of-art methods by a substantial margin, including those using multiple modalities. Moreover, we also evaluate the performance of our novel model on a Twitter sentiment dataset. We show that our model outperforms a general GNN model by leveraging our novel 2-D node attributes. These results demonstrate the generality of the proposed method.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is protected by copyright. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. | ||||||
Keywords: | Graph neural networks, sentiment analysis, node-embedding algorithm, diagnostic prediction task | ||||||
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Institution: | The University of Leeds | ||||||
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) | ||||||
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Depositing User: | Symplectic Publications | ||||||
Date Deposited: | 11 May 2022 14:49 | ||||||
Last Modified: | 05 Oct 2023 08:46 | ||||||
Published Version: | https://openreview.net/forum?id=OtEDS2NWhqa | ||||||
Status: | Published | ||||||
Publisher: | OpenReview |