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  • HealTAC2023_Paper

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

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Publication date16/06/2023
<mark>Original language</mark>English
EventHEALTHCARE TEXT ANALYTICS CONFERENCE 2023: PhD Forum - Manchester University, Manchester, United Kingdom
Duration: 15/06/202316/06/2023


Abbreviated titleHealTac 2023
Country/TerritoryUnited Kingdom
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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.