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

Ana-Maria Bucur, Hyewon Jang, Farhana Ferdousi Liza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
15 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
EditorsAyah Zirikly, Dana Atzil-Slonim, Maria Liakata, Steven Bedrick, Bart Desmet, Molly Ireland, Andrew Lee, Sean MacAvaney, Matthew Purver, Rebecca Resnik, Andrew Yates
PublisherAssociation for Computational Linguistics
Number of pages8
ISBN (Electronic)9781955917872
Publication statusPublished - Jul 2022

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