Detecting Depression in Social Media using Fine-Grained Emotions

Abstract

Nowadays social media platforms are the most popular way for people to share information, from work issues to personal matters. For example, people with health disorders tend to share their concerns for advice, support or simply to relieve suffering. This provides a great opportunity to proactively detect these users and refer them as soon as possible to professional help. We propose a new representation called Bag of Sub-Emotions (BoSE), which represents social media documents by a set of fine-grained emotions automatically generated using a lexical resource of emotions and subword embeddings. The proposed representation is evaluated in the task of depression detection. The results are encouraging; the usage of fine-grained emotions improved the results from a representation based on the core emotions and obtained competitive results in comparison to state of the art approaches.

A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media

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Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

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Suicidal Risk Detection for Military Personnel

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