Feasibility of Emotions as Features for Suicide Ideation Detection in Social Media

Arreerard, Ratchakrit and Piao, Scott (2023) Feasibility of Emotions as Features for Suicide Ideation Detection in Social Media. In: HEALTHCARE TEXT ANALYTICS CONFERENCE 2023, 2023-06-15 - 2023-06-16, Manchester University.

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Abstract

Suicide-related social media message detection is an important issue. Such messages can reveal a warning sign of suicidal behaviour. This paper examines the efficacy of using emotions as sole features to detect suicide-related messages. We investigated two methods which use a single emotion and a set of seven emotions as features respectively. For emotion classification, we used a classifier based on BERT named "Emotion English DistilRoBERTa-base". For detecting suicide-related messages, we tested Naive Bayes and Support Vector Machine. As our training/test data for suicide message detection, we used a publicly available dataset collected from Reddit in which each post is labelled as "suicide" or "non-suicide". Our method obtained accuracies of 76.2% and 76.8% for detecting suicide-related messages with Naive Bayes and Support Vector Machine respectively. Our experiment also shows that three emotion categories, "anger", "fear" and "sadness", have a strongest correlation with suicide-related messages.