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

Bucur, Ana-Maria, Jang, Hyewon and Liza, Farhana Ferdousi (2022) Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies. In: Proceedings of the 2022 The Eighth Workshop on Computational Linguistics and Clinical Psychology: Mental Health in the Face of Change ( In conjunction with the conference of the North American Chapter of the Association for Computational Linguistics: Huma. (In Press)

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Abstract

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: sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 21 Jun 2022 09:30
Last Modified: 04 Jul 2022 23:58
URI: https://ueaeprints.uea.ac.uk/id/eprint/85718
DOI:

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