DepressionEmo: A novel dataset for multilabel classification of
  depression emotions
Abu Bakar Siddiqur Rahman Hoang-Thang Ta Lotfollah Najjar Azad Azadmanesh Ali Saffet Gönül
Emotions are integral to human social interactions, with diverse responses
elicited by various situational contexts. Particularly, the prevalence of
negative emotional states has been correlated with negative outcomes for mental
health, necessitating a comprehensive analysis of their occurrence and impact
on individuals. In this paper, we introduce a novel dataset named DepressionEmo
designed to detect 8 emotions associated with depression by 6037 examples of
long Reddit user posts. This dataset was created through a majority vote over
inputs by zero-shot classifications from pre-trained models and validating the
quality by annotators and ChatGPT, exhibiting an acceptable level of interrater
reliability between annotators. The correlation between emotions, their
distribution over time, and linguistic analysis are conducted on DepressionEmo.
Besides, we provide several text classification methods classified into two
groups: machine learning methods such as SVM, XGBoost, and Light GBM; and deep
learning methods such as BERT, GAN-BERT, and BART. The pretrained BART model,
bart-base allows us to obtain the highest F1- Macro of 0.76, showing its
outperformance compared to other methods evaluated in our analysis. Across all
emotions, the highest F1-Macro value is achieved by suicide intent, indicating
a certain value of our dataset in identifying emotions in individuals with
depression symptoms through text analysis. The curated dataset is publicly
available at: https://github.com/abuBakarSiddiqurRahman/DepressionEmo.