2134/36066
Xuetong Chen
Xuetong
Chen
Martin Sykora
Martin
Sykora
Tom Jackson
Tom
Jackson
Suzanne Elayan
Suzanne
Elayan
Fehmidah Munir
Fehmidah
Munir
Tweeting your mental health: Exploration of different classifiers and features with emotional signals in identifying mental health conditions
Loughborough University
2018
untagged
Business and Management not elsewhere classified
2018-11-19 14:12:38
Conference contribution
https://repository.lboro.ac.uk/articles/conference_contribution/Tweeting_your_mental_health_Exploration_of_different_classifiers_and_features_with_emotional_signals_in_identifying_mental_health_conditions/9499409
Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion
related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions
as features and examined five popular machine learning classifiers in the task of identifying users with selfreported
mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features
and combined features showed promising improvements to the performance on this task.