# DepressionEmo: A novel dataset for multilabel classification of depression emotions

arXiv: https://arxiv.org/abs/2401.04655 · HTML: https://mlsanity.com/paper/2401.04655/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 84 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks](https://mlsanity.com/paper/2304.10145/) (2023)
- [Evaluation of ChatGPT for NLP-based Mental Health Applications](https://mlsanity.com/paper/2303.15727/) (2023)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences](https://mlsanity.com/paper/1804.07000/) (2018)
- [Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings](https://mlsanity.com/paper/2209.07494/) (2022)
- [Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis](https://mlsanity.com/paper/2306.17177/) (2023)
- [Towards Preemptive Detection of Depression and Anxiety in Twitter](https://mlsanity.com/paper/2011.05249/) (2020)
- [DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities](https://mlsanity.com/paper/2210.05372/) (2022)
- [Depression Severity Estimation from Multiple Modalities](https://mlsanity.com/paper/1711.06095/) (2017)
- [WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs](https://mlsanity.com/paper/2209.13101/) (2022)
- [Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN](https://mlsanity.com/paper/2302.02759/) (2023)
- [Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach](https://mlsanity.com/paper/2303.05389/) (2023)

## Built on by (most cited first, 10 of 10)

- [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024)
- [Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings](https://mlsanity.com/paper/2506.06616/) (2025)
- [Detecting anxiety and depression in dialogues: a multi-label and explainable approach](https://mlsanity.com/paper/2412.17651/) (2024)
- [Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning](https://mlsanity.com/paper/2405.01597/) (2024)
- [Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages](https://mlsanity.com/paper/2503.18253/) (2025)
- [Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement](https://mlsanity.com/paper/2601.13481/) (2026)
- [PsihoRo: Depression and Anxiety Romanian Text Corpus](https://mlsanity.com/paper/2602.18324/) (2026)
- [Depression Risk Assessment in Social Media via Large Language Models](https://mlsanity.com/paper/2604.19887/) (2026)
- [SuiChat-CN: Benchmarking Contextual Suicide Risk Assessment in Chinese Group Chats](https://mlsanity.com/paper/2605.27911/) (2026)
- [Comparing Chatbot Performance Enhanced with Persistent Homology](https://mlsanity.com/paper/2606.29857/) (2026)

## Abstract (arXiv)

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.