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
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
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 |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Alan Turing Institute No ref given EU - European Union 825619 |
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 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186629 |