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: this https URL

通过对 DepressionEmo 数据集进行情感、时间分布和语言分析，并使用文本分类方法，特别是 BERT 模型，研究表明该数据集在通过文本分析来识别抑郁症患者的情感上具有一定的价值，其中自杀意图情感所达到的最高 F1-Macro 值表明了这一点。