Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies

Bucur, Ana-Maria, Jang, Hyewon and Liza, Farhana Ferdousi ORCID: (2022) Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies. In: Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology. Association for Computational Linguistics, pp. 205-212. ISBN 9781955917872

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This paper presents the system description of team BLUE for Task A of the CLPsych 2022 Shared Task on identifying changes in mood and behaviour in longitudinal textual data. These moments of change are signals that can be used to screen and prevent suicide attempts. To detect these changes, we experimented with several text representation methods, such as TF-IDF, sentence embeddings, emotion-informed embeddings and several classical machine learning classifiers. We chose to submit three runs of ensemble systems based on maximum voting on the predictions from the best performing models. Of the nine participating teams in Task A, our team ranked second in the Precision-oriented Coverage-based Evaluation, with a score of 0.499. Our best system was an ensemble of Support Vector Machine, Logistic Regression, and Adaptive Boosting classifiers using emotion-informed embeddings as input representation that can model both the linguistic and emotional information found in users’ posts.

Item Type: Book Section
Uncontrolled Keywords: speech and hearing,language and linguistics,computer networks and communications,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/3600/3616
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 21 Jun 2022 09:30
Last Modified: 07 Oct 2023 01:29
DOI: 10.18653/v1/2022.clpsych-1.18


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