MM-EMOG: Multi-label Emotion Graph Representation for Mental Health Classification on Social Media

Anonymous

Abstract:

More than 80% of people who commit suicide disclose their intention to do so on social media. The main information we can use in social media is user-generated posts since personal information is not always available. Identifying all possible emotions in a single textual post is crucial to detecting the user's mental state; however, human emotions are very complex, and a single text instance likely expresses multiple emotions.
This paper proposes a new multi-label emotion graph representation for social media post-based mental health classification. We first construct a word-document graph tensor to describe emotion-based contextual representation using emotion lexicons. Then, it is trained by multi-label emotions and conducts a graph propagation for harmonising heterogeneous emotional information, and is applied to a textual graph mental health classification.
We perform extensive experiments on three publicly available social media mental health classification datasets, and the results show clear improvements.

Paper Type: short

Research Area: NLP Applications

Languages Studied: English

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