Robotics (Mar 2024)

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

  • Rina Carines Cabral,
  • Soyeon Caren Han,
  • Josiah Poon,
  • Goran Nenadic

DOI
https://doi.org/10.3390/robotics13030053
Journal volume & issue
Vol. 13, no. 3
p. 53

Abstract

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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.

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