Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data
North American Chapter of the Association for Computational Linguistics (NAACL) Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology , Washington, Amerika Birleşik Devletleri, 10 - 15 Temmuz 2022, ss.219-225, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.18653/v1/2022.clpsych-1.20
- Basıldığı Şehir: Washington
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.219-225
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet
Özet
In this shared task, we focus on detecting mental health signals in Reddit users’ posts through
two main challenges: A) capturing mood
changes (anomalies) from the longitudinal set
of posts (called timelines), and B) assessing
the users’ suicide risk-levels. Our approaches
leverage emotion recognition on linguistic
content by computing emotion/sentiment
scores using pre-trained BERTs on users’ posts
and feeding them to machine learning models,
including XGBoost, Bi-LSTM, and logistic
regression. For Task-A, we detect longitudinal
anomalies using a sequence-to-sequence
(seq2seq) autoencoder and capture regions
of mood deviations. For Task-B, our two
models utilize the BERT emotion/sentiment
scores. The first computes emotion bandwidths
and merges them with n-gram features, and
employs logistic regression to detect users’
suicide risk levels. The second model predicts
suicide risk on the timeline level using a BiLSTM on Task-A results and sentiment scores.
Our results outperformed most participating
teams and ranked in the top three in Task-A.
In Task-B, our methods surpass all others and
return the best macro and micro F1 scores.