README.md

---
pretty_name: "FineGrainedDepressionEmo"
tags:
  - text-classification
  - sentiment-analysis
  - mental-health
  - depression
  - emotions
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
language:
  - en
license: unknown
annotations_creators:
  - expert-generated
source_datasets:
  - original
paper:
  - ""
---

# Dataset Card for FineGrainedDepressionEmo

## Dataset Summary

FineGrainedDepressionEmo is a sentence-level emotion classification dataset
derived from long-form, depression-related user posts. Each post is split into
sentences and each sentence is annotated with a fine-grained emotion label
capturing distinct facets of depressive experience (e.g., sadness,
hopelessness, worthlessness, suicide intent).

The dataset is designed for studying fine-grained emotional signals in mental
health narratives and for evaluating context-aware models that operate over
sentence sequences within a thread.

## Supported Tasks and Leaderboards

**Primary task:** single-label multi-class emotion classification at sentence
level.

Given a sentence, the goal is to predict one of the following emotion labels:

- anger  
- brain dysfunction  
- emptiness  
- hopelessness  
- loneliness  
- sadness  
- suicide intent  
- worthlessness  

There is no official leaderboard yet, but the dataset is suitable for:

- Baseline sentence classification with BERT-like models (C0, no context).
- Context-aware models that include previous/next sentences or full threads
  (C1–C3 in the accompanying EmoShiftNet code).

## Languages

- English (informal, user-generated, often Reddit-style narratives).

## Dataset Structure

### Data Instances

Each row corresponds to a single sentence from a depression-related narrative.

Example (HF-sanitized version):

```text
item_id,sentence,emotion_final
hhcq6e_1,My mum had a boyfriend when I was around 6 or 7.,sadness
hhcq6e_2,She met him while she was volunteering at a prison.,sadness
```

### Data Fields

In the Hugging Face upload variant, each CSV file has exactly three columns:

- `item_id` (`string`): identifier for the sentence. Sentences from the same
  original post share a common base id, and in the HF-ready copy each sentence
  is made unique by appending a 1-based index within that thread
  (e.g., `hhcq6e_1`, `hhcq6e_2`, …). The numeric suffix reflects sentence
  order in the original narrative.
- `sentence` (`string`): the sentence text.
- `emotion_final` (`string`): the final human-validated emotion label, one of:
  `anger`, `brain dysfunction`, `emptiness`, `hopelessness`,
  `loneliness`, `sadness`, `suicide intent`, `worthlessness`.

### Data Splits

Typical files:

- `train_merged_final_emotions_with_final_label.csv`
- `val_merged_final_emotions_with_final_label.csv`
- `test_merged_final_emotions_with_final_label.csv`
- `all_merged_final_emotions_with_final_label.csv`

Split sizes depend on the exact configuration, but the combined file
(`all_merged_final_emotions_with_final_label.csv`) contains **32,347** labeled
sentences.

### Label Distribution

Label counts in the full combined file:

- sadness: 10,023  
- hopelessness: 6,494  
- loneliness: 5,107  
- anger: 4,168  
- worthlessness: 2,250  
- suicide intent: 1,821  
- emptiness: 1,720  
- brain dysfunction: 764  

The label distribution is moderately imbalanced; macro-averaged metrics (macro
F1) are recommended when reporting results.

### Text Characteristics

Sentences are mostly well-formed English, often written in first person and
describing subjective, depression-related experiences.

End-of-sentence punctuation in the combined file:

- `.` (period) — **27,507** sentences  
- `?` (question mark) — **2,260** sentences  
- `!` (exclamation mark) — **425** sentences  

All other final characters (letters, closing brackets, quotes, ellipsis, etc.)
are comparatively rare. This is helpful when choosing maximum sequence length
and when designing sentence-boundary-aware models.

## Data Processing and Annotations
 
High-level pipeline (see the EmoShiftNet repository for full details):
 
1. Collect long-form, depression-related posts.
2. Split posts into sentences.
3. Derive a final consensus / manually validated label `emotion_final`.
4. For the Hugging Face upload:
   - Remove intermediate model-based label columns.
   - Make `item_id` sentence-unique via a per-thread index suffix.

## Usage

In Python with `datasets`:

```python
from datasets import load_dataset

ds = load_dataset("samanjoy2/FineGrainedDepressionEmo")
df = ds["train"].to_pandas()  # if you define train/val/test splits
```

Typical modeling steps:

1. Use `sentence` as input text.
2. Encode `emotion_final` as class labels.
3. Optionally recover base thread ids by stripping the numeric suffix from
   `item_id` (e.g., `hhcq6e_1` → `hhcq6e`) and group sentences to build
   context-aware inputs (previous/next sentences, full thread, etc.).

## Ethical Considerations

The dataset originates from user-generated content describing mental health and
depression-related experiences.

Users should:

- Avoid attempts to identify or deanonymize individuals.
- Avoid deploying models trained on this data in high-stakes clinical settings
  without appropriate oversight.
- Clearly communicate limitations and potential biases of any models trained on
  this dataset.

## Citation

If you use this dataset in academic work, please cite the associated EmoShiftNet
paper or repository (add citation details here when available).